Published by Dranetz Technologies, Inc., Case Study
Energy and demand costs have a direct impact on a company’s bottom line. In fact, the cost of energy is one of the most commonly mismanaged expenses, regardless of a company’s size or the industry represented. In healthcare facilities energy management can be more difficult than any other industry, because you not only have to manage expenses, but you’re also dealing with managing human lives. Additionally, managing so many different functions within a single operation requires extra work to manage and measure energy improvements. Managing energy expenses does not have to come at a cost of sacrificing patient safety.
A typical facility can save from 10% to as much as 40% annually on energy costs by implementing a comprehensive energy action plan. And, while each facility will require an organized approach to recognize those savings, facilities first need to understand their power consumption, location of major loads, electric demand usage patterns, and associated costs.
A well organized monitoring and reporting system allows determination of where energy is going, identifies the biggest users, and decides which areas are likely to reap the largest benefits from energy management efforts while managing the impacts on patient safety. The benefits from applying monitoring and reporting principles and guidelines can be seen by comparing theoretical, equipment, system, and actual kWh/unit of production as a function of time.
Because a healthcare facility operates so many different areas and operations, the units of production will vary by department. As an example, the Xray, CT, MRI Test Department may be measured on the number of tests performed per hour, day or week vs. the amount of energy consumed; the Emergency Room (ER) would be measured by the number of patients treated per hour; and the Intensive Care Unit would be measured based on the amount of occupied patient beds per hour. Subsequently, in order to realize measurable improvements in energy conservation, it is essential to measure each of the various areas and apply the proper unit of production accordingly.
Benefits of Energy Monitoring Systems:
• Energy expenditure reductions: Load profile can be generated to track daily, weekly and seasonal variations in energy consumption, while critical loads can be metered and sub-metered to evaluate consumption and reduce energy costs.
• Allocate Costs and Perform Activity-based Costing: Track energy-related costs by department, tenant, process or output. Revenue-accurate metering allows for easy cost comparison with utility bills.
• Manage Energy Purchase Agreements: Use historical load profile data to develop price/risk curves for evaluating energy purchase agreements or for joining an aggregated group to purchase power at reduced costs.
• Perform Energy Conservation and Load Reduction: Shed non-essential loads or bring distributed generation on line to reduce consumption and/or participate in utility-sponsored demand reduction programs. Evaluate the value of energy efficient equipment such as lighting and HVAC changes.
• Reduce Demand Peaks and Related Costs: Avoid demand surcharges to predict kW demand and identify the cause of demand peaks and limit peak occurrences. Generate alarms on events such as excessive load, equipment failure, or when operations are likely to exceed contract terms for energy supply.
• Evaluate Impact of Production Equipment on Energy Costs: Monitor the efficiency of large, energy-consuming equipment to improve performance. Plan for expansion by analyzing load trends and available capacity for new equipment.
Published by Mahmood T. Alkhayyat1, Laith A. Khalaf2, Mohammed Y. Suliman3, Northern Technical University. ORCID: 1. 0000-0001-6119-7845; 2. 0000-0003-1602-6152; 3. 0000-0002-1250-6362
Abstract. A special form of energy system that can be utilized to provide all the energy needed in the globe is the renewable hybrid system. In order to successfully use renewable energy and decrease the amount of energy drawn from the power grid, a micro-grid management technique based on renewable energy has been developed in this study. The utilization of renewable energy sources, such as solar energy from photovoltaic panels and wind energy (from wind turbines), may run loads more effectively while consuming less fuel. To regulate the electricity, an adaptive control system was also created. The performance of the suggested control method for managing power flow is demonstrated by simulation results acquired using MATLAB/Simulink in a variety of operating modes.
Streszczenie. Specjalną formą systemu energetycznego, który może być wykorzystany do dostarczenia całej energii potrzebnej na świecie, jest odnawialny system hybrydowy. W celu skutecznego wykorzystania energii odnawialnej i zmniejszenia ilości energii pobieranej z sieci elektroenergetycznej, w niniejszym opracowaniu opracowano technikę zarządzania mikrosieciami opartą na energii odnawialnej. Wykorzystanie odnawialnych źródeł energii, takich jak energia słoneczna z paneli fotowoltaicznych i energia wiatrowa (z turbin wiatrowych), może efektywniej obsługiwać obciążenia przy mniejszym zużyciu paliwa. Aby regulować energię elektryczną, stworzono również adaptacyjny system sterowania. Wydajność proponowanej metody sterowania przepływem mocy została zademonstrowana na podstawie wyników symulacji uzyskanych przy użyciu MATLAB/Simulink w różnych trybach pracy. (Sterowanie adaptacyjne do zarządzania energią w oparciu o energię odnawialną)
Keywords: power management, hybrid system, distribution generators, renewable energy. Słowa kluczowe: zarządzanie energią, system hybrydowy, generatory dystrybucyjne, energia odnawialna.
Introduction
The request for power increments quickly when compared to the era. Subsequently, there’s a requirement for an elective approach to fulfill the request. As “Energy Moderated is Break even with to Vitality Created”. Most customary power-generating plants run on fossil control and deliver 64.5% of control around the world [1]. These control plants have a greater share in carbon outpourings, where generally 40% of carbon is transmitted by the period division, and the transport division [2] produces 24%. Besides, to reach the radically expanding vitality request with lower carbon outflows, analysts have proposed unused strategies of vitality era utilizing renewable vitality sources (RESs). we are centring on diverse ways to control the utilization of vitality this leads to the concept of Stack Side Administration. Request administration alters shopper request through diverse strategies like Smart-grid (SG)operation strategies [3]. SG is characterized as it may be a control supply organization that can cleverly connect the exercises of all clients associated with it like generators, buyers, and prosumers (all those do both time and utilization). SGs work with unmistakable sorts of contraptions such as quick meters (SMs), quick machines, RESs, and batteries. Due to stresses approximately the environment and the economy, there has been an increase in intrigued within the creation and administration of renewable vitality in later a long time [4]. The first move is Hybrid wind was the first renewable energy source to be integrated. as supplementary sources, and solar systems as a remedy for applications in remote areas and shaky grid connections. Further hybrid systems, including a number of them, have been used in study. sources of miniaturized renewable energy including solar thermal, biomass, tidal power and fuel cells [5]. Following the output price to wind turbine and solar panel uses of decreased were drastically decreased, and now they are the only option. for systems that generate hybrid energy [6]. The most objective is to encourage buyers to play down the utilization of control amid top hours, but this cannot be done abruptly. Then again, renewable vitality sources can be coordinated with the framework which decreases the control expended by the framework [7]. The term “Smart Grid” refers to this connection. Since production is now decentralized, the new system improves electricity quality, which is the primary driver of institutions’ dependability of the power system, smart grids (SG) connect small power production units, mostly from renewable sources, and employ sophisticated control technology [8]. Right now, renewable vitality share around the world is as it were 11% whereas it is anticipated to extend by 60% in 2070 [9]. The worldwide capacity of wind and sun-oriented photovoltaic (PV) is expanded to 514.8 GW and 399.6 GW individually [10]. Figure 1 appears the global trend for speculation within the PV and wind renewable vitality divisions [11]. In this study, the styling of a smart grid for host structures is given as shown in Figure 2. The submitted smart grids central power sources are wind turbines and photovoltaic for sun and the main grid supply. A management system is constructed to keep track of and oversight the loads into a structure for to achieve the right equilibrium among the energy created and consumed. Adaptive neuro fuzzy system designed to manage the power delivered to the consumer with the priority to the renewable energy (solar and wind energies) over the grid.
An administration practices is created to keep off a delightful putting right between the undertaking around and meander which is required by the building’s loads. A supervision renewable fight is explained in Figures in [12] location an Egyptian verdant bailiwick organic upon a surly PV/WT/FC to the electrical utility [13]. The blend sincerely moves forward the profit bizarre composition these interface GESs to its possess dynamic more favourably on touching GES taken a toll thought [14]. Alternate study based on the optimum approximation of a hybrid GES cryptogram in Egypt was presented in effort [15]. In reference [16] investigated the used profits of adding renewable ways with the electrical network on provincial action efficiency. In [17] had beholden a commensurability centre of utility clarification and involve date (DG) alternates to decipher the problem of talent grid outages in rural area. The authors in [18] faked the fusing of GES to the electric utility with energy make consistent and environmental problems consideration. In [19] transferable and shred infested with optimization (PSO) mechanisms were applied to economic studies of off-grid GES. This assembly factual adaptive administer obstruction permission neuro-fuzzy feud supply to superintend the power dog-tired to the pressure by weaken the power grid by compensating power from GES utilized PV/WT energies.
Fig.1. Global trends of renewables (a) solar generation (b) wind generation 2010-2020 [5]
The grid-tied system
The PV and WT frameworks make up the two-source renewable vitality system that’s talked about in this issue. The essential neighbourhood framework is associated with these renewable vitality sources. An inverter for PV/WT is portrayed in Figure 2.
Fig.2. Hybrid energy system
Main local grid
The grid is depicted as an energy source that has the capacity to produce and absorb electric power. The constraints put on power transformers, transmission lines, and other heavy power equipment of the grid have a limit on how much energy can be absorbed or delivered in a particular time period.
PV vitality framework model
The created electric control from the PVES is influenced significantly by the irradiance falling on the PV as well as its area. To extend the irradiance and subsequently, the created vitality from the PV framework it is suggested to tilt the PV modules with a perfect tilt point. This perfect tilt point is chosen to break indeed with the scope point of the area [20]. The hourly produced control from the PV cluster can be decided by the taking after condition [21]:
.
Where: PPV Produced power of photo voltaic (kW); Prated Output power of module (kW); NPV PV modules number; Df PV derating factor ; G Titled plane global utility fallen (kW/m2); Gref Solar radiation at base conditions (1 kW/m2); KT Temperature coefficient ; Tamb Ambient temperature (◦C); NWCT Normal working cell temperature
The solar model shown in Figure 3.
Fig.3. PV energy system model
The height of the anemometer, which should be adjusted to the height of the wind turbine, is used to measure wind speed [22]. Equations illustrate the relationship between the anemometer height and the wind speed to any height are:
.
Where: Pw Produced power of the wind turbine (kW); NWT Number of the wind turbines; Pr Rated wind turbine power (kW); Vci Cut-in wind speed (m/s); Vco Cut-on wind speed (m/s); Vr Rated wind speed (m/s); ηw Wind turbine efficiency
The wind model shown in Figure 4.
Fig.4. Wind energy system model
Inverter
Both the WT and PV framework create DC vitality. A DC association is joined to the two GREs’ DC yield [23]. An inverter is required to put through this DC connection to the electrical lattice at the AC transport. Due to the reality that the proposed on-grid half-breed framework consolidates a battery (as a storage framework), the inverter utilized may be a bi-direction sort to empower charging the batteries from the AC side within the occasion that the REGs’ yield control is deficient, as already portrayed in Figure 2. The prerequisite for crest stack for the most part decides the measure of the inverter:
.
Where: Pinv Output power (kW); PLmax Demand power (kW); ηinv Inverter efficiency
Grid power system model
The grid is shown as an energy source that has the capacity to produce and absorb electric power [23]. There is a limit on how much energy can be absorbed or delivered in a given time period due to the limitations placed on power transformers, transmission lines, and other overwhelming control gear of the network. A Diesel Generator DG is utilized to fulfill a high-power shortage amid the 24-hour day to supply the stack. Depending upon the fuel utilization of DG can be assessed the yield control [24]:
.
Where: PDG diesel generator output (kW); FDG rate of fuel consumption in Ltr/hr; α coefficient of fuel intercept in Ltr/kWh; β fuel slope in Ltr/kWh; CDG capacity of diesel generator in kW
Adaptive control design
Fuzzy logic controllers (FLC) are appropriate for uncertain systems, particularly for systems whose mathematical models are challenging to generate. In a variety of real-world applications, FLC is crucial [25]. There are many other kinds of fuzzy inference systems, however in this study the Takagi-Sugeno TS-fuzzy is used. The Artificial Neural Network (ANN) will be employed with a TS-fuzzy- like-PI controller to modify the membership functions. This kind (TS-fuzzy controller) features a highly nonlinear controller with variable gain, which results in a large range of changes in the controller’s gain. Randomly selecting the controller settings may result in a system with an adequate in responsiveness or unitability. By combining ANN with fuzzy logic in a neuro-fuzzy system to change the controller’s settings, it is possible to get a better response [26].
By using an artificial neural network (ANN) as a learning method, fuzzy rules are learned. Without the expert knowledge often necessary for the usual form of FCL, such control may be learned, and the rules base can be reduced. Training phases establish the input control parameters and output of membership functions (MF). The goal of the learning algorithm is to modify the input parameters and output MFs to get the optimal output alignment for the proposed controller [27]. The control parameters of a network are often identified using a hybrid learning technique called “Least Squares Estimate-LSE and Gradient Descent-GD” [28]. In the current study, the controller grid power’s two inputs were generated by solar and wind energy. Wind turbine power and PV power with one output for power load decision to the control unit of DG the structure as shown in Figure 5. Structure breaks the universe for discourse into three-triangle MFs with a 50% overlap, followed by controller inputs and control p controller generates “three inputs and one output”, with a control rule of 9 as a result, requiring the specification of linear functions. One piece of data must be produced in order to tweak the TS rules using neuro-fuzzy. The controller’s input data consists of data vectors with PV/WT results that follow Equations 1 and 2, with the controller’s output going to the DG control unit. Figure 6 depicts the control designer’s output validation surface. The MATLABGUI for neuro-fuzzy file contained in the toolbox of MATLAB/fuzzy is employed to carry out the training operation.
Fig.5. Neuro-fuzzy control structure
Fig.6. Wind energy system model
Simulation results
The proposed system model consists of feeder with load branch of 14 MW. Two scenarios are investigated as:
Scenario I
The supplied by three sources the main grid supply with 15 MW, and variable wind energy with maximum power of 4.5 MW as shown in Figure 6 and solar energy with 8 MW in time interval from 6 am to 18 pm as shown in Figure 7. The adaptive neuro-fuzzy controller programmed to give the priority for supply for wind and solar energies to decrease the supply from the grid this will decrease the fuel and then operate more economic. The proposed power management of the system algorithm is illustrated in Figure 8. The time calculated based on 24 hours (606024 sec). The real and reactive power demand of the load for 24 hours is shown in Figure 9, also the load voltage shown in Figure 10 respectively.
Fig.7. Solar/wind power per day (scenario I)
Fig.8. A proposed power management philosophy
Decrease the supply from the grid to obtain more economy operation the increase the supply from wind and solar at their peak’s operation 8 MW for solar and 4.5 MW for wind the summation of the two renewable energy is about 12.5 MW and this will compensate the load when increased to 1.5 pu about 21 MW where decrease the supply from the grid to less than 0.5 pu about 7 MW at time 4.
Fig.9. Load demand power
Fig.10. Load voltage
Managed powers of grid, wind and solar shown in Figure 11.The test starts by feeding the load first by the main grid supply at time 1.45 the wind energy starts to share the load and increase to supply causing to decrease the grid supply and become more economy, at time 2.5 the solar energy starts and share the load with the wind energy and decrease the supply from the grid to obtain more economy operation the increase the supply from wind and solar at their peaks operation 8 MW for solar and 4.5 MW for wind the summation of the two renewable energy is about 12.5 MW and this will compensate the load when increased to 1.5 pu about 21 MW where decrease the supply from the grid to less than 0.5 pu about 7 MW at time 4 . the powers scenario of grid, wind and solar shown in Figure 11.
Fig.11. The managed powers per day (scenario I)
From Figure 11 it can be notice that decreasing the fossil fuel stations by using renewable energy, the compensate the power from renewable energy reach about 50% and this will decrease the total cost of operation.
Scenario II
Wind power and solar power are varied as depicted in Figures 12. Managed power is shown in Figure 13.
Fig.12. Wind power and solar power
Fig.13. The managed powers per day (scenario II)
Conclusion
In this work, adaptive control designed for grid-tied hybrid clean energy is presented by MATLAB simulation. A system includes solar cells, a wind turbine, and a diesel generator. Adaptive neuro-fuzzy controller proposed to manage the power feeding to the load. Simulation results guarantee the optimum operation and energy management. The theory used in this work is to reduce the economic expenses of a PV/wind/ hybrid system connected with the grid while also enhancing the system for potential variations in load. Two scenarios are proposed to validate the theory of control system. The results shows an appropriate solution for hybrid renewable energy connected to the grid.
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Authors: dr M.T. Alkhayyat. Mosul, Iraq, Northern Technical University, E-mail: m.t.alkhayyat@ntu.edu.iq, Laith A. Khalaf, Northern Technical University, E-mail: laith.abd@ntu.edu.iq, dr Mohammed Y. Suliman, Mosul, Iraq, Northern Technical University, E-mail: mohammed.yahya@ntu.edu.iq.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 99 NR 12/2023. doi:10.15199/48.2023.12.19
Published by Dranetz Technologies, Inc., Case Study
Data Center uses Encore to monitor Demand Response Performance
A 1.2M square ft Data Center located in Northern New Jersey was participating in a Demand Response program utilizing their backup generators as the primary means of reducing their 7MW of load. The service provider that they worked with installed telemetry metering from the utilities KYZ pulse output to report the performance to the grid operator. However, the facility director wanted live access to the generators output so that they could measure the performance of the generators and watch loading on each of the four 2.5MW turbines in real-time and historically. And, it had to painlessly integrate into his existing BMS system over an IP backbone.
Backup generators are critical to the operation of a 24×7 facility, downtime is lost revenue and lost customers. A facility operator needs to know what’s happening to his system all the time, and electricity is the main backbone of his entire operation. Let’s face it, no matter how many T1’s are coming in from different carriers and different POP’s, if the electricity goes out everything stops working including communications.
Utilizing the Encore Series ES230 DataNode’s along with their existing Encore Series Software installation, the Facility Director was able to give his BMS team a Modbus map for the instruments which were easily programmed. Because these were backup generators installation was much easier, however they were done sequentially in bypass mode because no one knows if or when the power will go out and the engines would be needed for an emergency. After the installation was complete and the integration into the BMS was done, the facility manager performed a live test and the results were perfect. The facilities network operations center (NOC) was able to read the instantaneous values from the generators from their control room, the ES230’s were also integrated into the existing Encore Series Software system so the facilities engineers also gained that they were able to see all of the instruments remotely when needed.
The graphs below display how easy it is for a user to view their reports on energy usage, demand, and any other electrical parameters recorded in the Encore Series Software from any remote location.
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The ES230 DataNode’s are small and easy to install and configurable either at the local display or through the Encore Series Software. These instruments are capable of recording Volts, Amps, kW, kWh, kVA, kVAR, Power Factor, Harmonics, and a variety of other parameters simultaneously. They also have the option of RS232, RS485, or Ethernet communications and support native Modbus protocols. The Encore Series Software is a web enabled application that does not require the installation of any software on a local users computer. The system can be accessed from any web-enabled browser by multiple people simultaneously, and performs a variety of operations, including; data collection, data analysis, reporting, alarming, and remote setup of the equipment. With the Modbus driver installed the software can easily read data from any previously installed instrument that supports the Modbus protocol. Additionally, the software allows for easy expansion, including the addition of Power Quality instruments for more detailed analysis of power anomalies.
Published by Tomasz SIEŃKO1, Jerzy SZCZEPANIK2, Cracow University of Technology, Krakow Poland (1), Cracow University of Technology, Krakow Poland (2) ORCID: 1. 0000-0002-3645-5694; 2. 0000-0001-5633-8359
Abstract. Increased Penetration of the Polish Power Supply System by renewable sources (RES) leads to a number of serious and new problems. The problems are associated with evacuating energy from producers (particularly visible for PV installations), change in the direction of power flow in the lines of the transmission and distribution system, problems with balancing energy production in the system related to uncertainty production of RES and a limitations in the possibility of control range of classic power plants. The current EU legislation leading to the increase of RES penetration will cause a significant rise appearance of in these problems. In principle these problems are unsolvable without the use of effective and achievable on a large scale methods and technologies of energy storage. The article estimates the generation level, generation variability and possible shortages in wind energy production in Poland in the case of the expansion of wind farms into offshore farms on the Baltic Sea. Data used in paper are based on some years of work inland RES in Poland available from PSE site. The idea is to adjust an operation of the power system to the basic formula of energy production and consumption balance with the required stability margins. A simple algorithm for estimating the size of the energy reserve necessary to stabilize the operation of the power system has been proposed. An important problem is also the assessment of the “geographical premium” (location). Theoretically, with the growth of the analyzed RES production area, the stability of DER production should increase since it become independent from purely local weather conditions.
Streszczenie. Wzrost Penetracji Polskiego Systemu Elektroenergetycznego przez źródła odnawialne (OZE) prowadzi do szeregu problemów: kłopotów wyprowadzeniem energii od producentów (szczególnie widoczne dla instalacji PV), zmiana kierunku przepływu mocy w liniach systemu przesyłowego i rozdzielczego, kłopoty z bilansowaniem systemu związane z niepewnością produkcji OZE oraz ograniczeniem w możliwości regulacji klasycznych elektrowni. Obecne prawodawstwo UE spowoduje znaczny wzrost tych problemów, w zasadzie nierozwiązywalnych bez efektywnego i olbrzymiego składowania energii w SEE. W artykule oszacowano poziom generacji, zmienność generacji oraz możliwe niedobory w produkcji energii wiatrowej (produkcja DER) w Polsce w przypadku rozbudowy farm wiatrowych na farmy morskie nad Bałtykiem. Ideą jest dostosowanie pracy systemu elektroenergetycznego do podstawowego bilansu produkcji i zużycia energii z wymaganymi marginesami stabilności. Zaproponowano prosty algorytm szacowania wielkości rezerwy energii niezbędnej do ustabilizowania pracy systemu elektroenergetycznego. Istotnym problemem jest również ocena „premii geograficznej” (lokalizacji). Teoretycznie wraz ze wzrostem analizowanego obszaru powinna wzrastać stabilność produkcji DER (niezależność od czysto lokalnych zjawisk pogodowych). (Rosnąca penetracja OZE do Systemu Elektroenergetycznego w Polsce)
Keywords: distributed energy resources, power system, wind power generation, power system stability, power system reserves. Słowa kluczowe: rozproszone źródła energii, system elektroenergetyczny, generacja energii z wiatru, stabilność systemu elektroenergetycznego, zapasy energii w systemie elektroeneretycznym.
Introduction
Until today, in power system, the energy needs by system loads has to be met by energy production to obtain constant frequency and voltages at system nodes. Of course some changes of those quantities are possible, but they are limited by system energy quality and stability constraints standards. Figure 1 shows the structure of production, usage and planned storage of energy in power system in Poland.
In general, two power systems operations modes are possible: one when system energy production and usage is balanced and energy is delivered to all customers constantly and uninterrupted according to the energy quality standard and second operation mode where breaks and disturbances in power delivery will be acceptable for less demanding applications and customers. This means, that in second case, customers has to be divided into ones where delivery breaks are possible and to ones where brakeless energy delivery is required. First group include mainly housing estate and second one is mainly industry and loads where continuous supply is required and customers such as hospitals, road traffic, rail power supplies etc. Such segregation of customers will be done on relatively low voltage and it has to work similar to SCO (self-acting frequency-dependent system unload who can disconnect the certain groups of customers from a supply). Legal regulations together with technical instructions still exist in Poland and they are related to the situation energy shortages at the turn of the 1970s and 1980s. Usually local operators can disconnect certain medium voltage feeders outgoing from high to medium voltage stations (in Poland is called GPZ – main supply 110/15 kV stations) to balance energy flow.
This modes of work of supply network should be discussed by the inhabitants of a given area.
The second mode of power system work is quite possible even now, when the supply from DER’s sources is on relatively high level and due the weather conditions it will rapidly decrease, and level supplied from basic power stations and existing reserve will be too small or to slow to balance system. Unfortunately, in the most of the analyses of wind energy costs as for example in the works [1], [2] the cost assessment ignores the cost of reserves and does not consider the real variability of energy production in the area [3]. The cost ocean, which in the case of grid connection analysis is an acceptable simplification but in in the case of the EEE stability analysis, no. It should be mentioned that in this case when support from neighbouring countries is possible (look at the failure of the energy supply to system from the Bełchatów power plant – 3500MW (compare to about 18000MW produced by whole coal using power plants). The breakdown lasted fiew hours and do not influenced system work due to support from power systems of neighbouring countries. Especially small support was given by German power system where large amount of energy is produced by DER.
As we can see two concepts of system work are possible and at the end, for both cases, the balance of the supply system is achieved in different ways- by increasing the energy production, or by decrease its load for a certain time. The increase of energy delivery is usually done by employing system reserves or by increasing energy import from adjacent countries (if it is possible).
Fig.1. Schematic of energy flow in polish power system with predicted extra energy storage for energy surplus from renewable sources.
Fig.2. Typical windmill power characteristics – X axle wind speed, Y axle relative (per unit) windmill power
The second mode of power system work include not only increase of the production but also the decrease of energy usage for a certain time needed to balance system. The observation of power flow on the PSE (Polish System Operator) internet site clearly shows large influence of intersystem flows from neighbouring countries in Polish system balance even during relatively calm times (small changes in energy usage and production from conventional power stations). Questions is if they will be enough to create reserve for new installations of DER sources since new massive investments at Baltic Sea.
Offshore windmills have different characteristics than onshore ones – utilization rate up to 40% and more stable production. Thus in principle, they require a separate analysis and later a possible analysis of cooperation for onshore and off shore cooperation. Analysing available data, one has to think about windmills located onshore right next to the sea – maybe some of their share resulted in the formed used factor of 0.26 instead of 0.22-0.24 for windmills located deep onshore what appears in several publications.
DER resources production data variability and Analysis of wind and solar energy production for existing data
DER sources energy production depends not only on weather conditions, but also on resources characteristics. They are non- linear functions of wind speed for windmills (figure.2) and also for solar modules the dependence of energy production on solar radiation is highly non-linear. Moreover solar panels do not work during the night and windmills do not produce energy when wind speed is lower than their starting point.
DER sources in Poland are mainly windmills and photovoltaic panels. The available data to estimate production of those sources and their dynamics are based on:
Data from meteorological stations regarding wind and solar operation. The data from those sources are generally from inhabited areas inside landmass and are affected by local conditions (wind and solar station location). This happens especially in Poland where majority of DER stations right now are located inland (urban conditions)and they are separated from the location of meteorological stations [4] To estimate DER production additionally one has to include sources distribution and characteristics (regardless of the meteorological data). The advantage of these sources are long series of measurement data, but unfortunately moderately useful in the light of the impact of climate change [5].
New wave measurements based on satellite systems data – available for every part of the globe as the continuous measurements –for a certain areas which are available due to satellite placement. The measurements data are more area dependent and usually available for a certain area and also these data can contain historical information for a certain areas which are available. The problems which are associated to DER usage are: Energy production estimation is similar to shown in previous section – this information is used in DER expansion planning, production forecasting and modelling. The measurements are available in [6].
Information from energy markets regarding present DER energy production and maximisation of this production according to European energy low. The information delivered by the energy markets contained hidden data about DER distribution at a certain area – in existing onshore locations, beneficial locations were already identified and for average characteristics of DER optimal energy production was calculated. This limits the expected changes for those windmills locations. The EU legal situation is as follows: each production of DER sources must be purchased by the electricity system operator in accordance with the provisions of EU law [7] [8]. This means that the market data about DER generation in local power subsystems should be very close to maximum temporary energy production which depend on actual weather conditions. When analysing the impact of the law on DER sources energy production it is worth to compare the data available from European operators [9], [10], and Californian operators [11].
Analysis of electrical energy production by wind energy at the area of Poland (PSE DATA)
The analysis of the energy production by windmills was done on basic on available old PSE data from period of time between 01.01.2013 to 12.31.2021. The choice of data was dictated by relatively large power of windmills installed during this time it ranged from 3 to 7 GW (During this time the windmills were installed only inland) The available data about windmills production are shown in Figure 3. The available interval of data is one hour for the time period under consideration.
Fig.3. Power delivered by windmills to Polish power system in years 2014-2021 – x axis time in years, y axis – production of power in GW; data with one hour interval
Fig.4. Windmill power installed in Poland in GW during the years (according [12] and [13]).
The figure shows the increase of windmill power associated to the increase of yearly installed production facilities. Thus, further analysis required data normalization with respect to the power of installed windmills [12],[13]. Powers of installed windmills are available at monthly intervals in figure 4. To normalize data windmill power production during the month was divided by the number of windmills installed and able to give power during this month. The information about the normalized production (coefficient of windmill power utilization), its variability and degree of its usage are compared to maximum the power of installed windmills what is shown in Figure 5.A.
It should be noted that the maximum possible production of wind energy could be higher than in this diagram since energy absorption by power system is limited by the stability requirements or local power transmission possibilities (overload of system elements). The legal situation forces the consumption of all possible renewable energy, so probably the minimum values and the slope of the falling slopes are more error-prone than the values of the maximum values and the slope of the rising slopes (the value sought and estimated is rather the maximum possible use of wind energy at a given moment).
Based on the energy production availability data, the following data were also estimated: median (median value from normalized values of function of windmills usage) – 0.2057; mean value -0.2652; effective value (RMS) -0.3388; maximum value –0.9609; and minimum-0.0014; and the production was averaged in annual intervals – the results are presented in the Figure 6
Fig.5. Normalized variation and utilization rate of production for windmills in years 2014-2021 -X axis time years, y axis currently obtained power in relation to installed power. Its cumulative distribution function is shown in figure b)- X axis Value, Y axis cumulative probability (probability that the value will be lower than on the axle X)
Mathematic elements of the analysis
The averaging of energy production by windmills was performed in yearly periods according to the following algorithm:
.
The analogic times intervals were used when the median values were calculated – they and shown in figure 6.
Visible not too large fluctuations in the average usage in each calculation period can be justified by opening a new wind farm with a slightly higher or lower (than the average for the system) average utilization of windmills related to better location or better use of turbines which adaptation to local conditions are improved or the influence of cyclical or cyclical quasi climatic oscillations with a period of phenomenon different from one year (like for example ELNINO).
Fig.6. Mean value (blue) and median one(red) of the coefficient of windmill power utilization in wind farms connected to the Polish power system, averaged over an annual moving range. X axis beginnings of averaging interval (hour from the beginning of the measurement series), Y axis mean (blue line) value and median value (red line).
It should be recalled that due to the non-linear characteristics of the windmill shown in Fig.2 small changes in wind speed can translate into significant changes in energy generation. This fact influences median that way that it is smaller than average value (widely dispersed values of energy production) [fig. 6].
Analyzing Figures 5 and 6, it should be noted that wind generation in Poland is characterized by a low coefficient of windmill power utilization thus the median value (i.e. the value for which 50% of samples are of lower value) falls to the levels of 0.16 to 0.24 and are interrupted by (relatively short) episodes of large energy production. The generation value during these episodes raises the average value from 0.22 to 0.3.
The second important parameter allowing to determine the level of ‘stress’ (need for use reserves or local blackout for power system balance) of electrical power system related to the operation of DER is the speed of energy production changes.
The analysis was performed according to the formula:
.
The results are presented in the figure 7 and were calculated in order to capture possible correlations between the volume of production and the size and speed of changes. No regularity was found. In order to confirm the analysis, a comparison of the relative variability of production to production value was compared (fig 8). Again, no other regularities were found.
Analysis of production charts and production variation charts indicates that wind energy production can possess very high dynamics and be highly dependent on weather conditions. As the penetration of these types of sources in the system structure increases, the amount of “stress” generated by them in power system will increase. It is especially related to the need to fast replacement. For example, if 10% -20% of the power generated by DER has to be replaced by other sources, not only it requires fast increase of the energy production in fully controllable power stations or reserve but also can introduce limitations of demand (by use local blackout or spinning blackout). The probability of the DER power change can be estimated on the basis of the figure 7b.
Fig.7. Absolute variability of the windmills production with respect to actual power (a) and its cumulative distribution function (b)
The scope of the analyzes of the impact of variability of wind energy generation on the operation of EPS covers the current situation (as in the paper [14]). However it is assumed that the lack of energy from these sources does not lead to serious disturbances in the operation of EPS what does not have to be true during high content of renewable resources in EPS energy production balance. It is worth to note that a high level of ‘stress’ will be associated with a situation, where a large energy production change occurs with high DER generation. In this situation the level of power system reserves may turn out to be insufficient. Due to the increase in DER generation, conventional generation consequently decreases and its possible reserves become limited. Thus, as installed power of DER grow, the probability of using SCO increases. An increase of DER power may also entail an increase in nonused energy – an increase in production or the speed of growth will not allow it to be used or stored for future use. At this point, the shape of the distribution function (symmetric with respect to 0) proves that the power system has no problems with absorbing this energy (assuming that the magnitude of the coal based generation change). Assuming that probability of wind change is similar in both directions (figure 7b)- we do not observe a cut in the upper part distribution associated with possible limitations of the power absorption by system, but so far as seen in the figure 7b the expected biggest change in generation has not exceeded 1.4 GW (20% installed windmill power) in absolute numbers.
The probability of using SCO (frequency load limiter) is also a function of the amount of windmills power installed, electricity production technologies used, existing power system reserves and the time horizon in which we are able to predict a certain event and time length of this event what depends on the weather prediction.
Fig.8. The relationship between the current power of the windmill and its change (X axis power in a given hour and Y axis change in a given hour) during whole year
There are many studies analyzing the production from DER in various timescales and geographical wind statistics [15 – 20]. They are usually assessing the production variability [21] and the possibility of its prediction [22-28], however there are no studies on the impact of DER generation instability on work EPS as a whole with a significant share of these sources (in the EU up to 40% of energy is generated from DER). Therefore, an attempt was made to assess the possible “stress” generated by windmills in the Polish SEE without cross-border cooperation.
Table 1. Energy shortage caused by changes in windmills production
.
In order to capture the parameters of the “stress” and possibility of power system response to it (voltage unbalance, frequency changes, power shortage, change in power distribution, required reserves energy and total power in system) and related to cooperation with windmills, several analyzes were performed for arbitrarily selected levels of windmills utilization – 5%, 10%, 15%, 20% and 50%. Even for level of utilization lower than 5% the cases of shortage of production longer than 24 hours are quite common. Thus, the disregard of daily variability and choosing average daily demand do not introduce large errors during analysis.
The table (1) below indicates the values of changes of energy as production of windmills deceases for a certain level of windmills productions, certain time lengths of events, median time of the event, relative mean energy of the scarcity, median of the relative energy scarcity and maximum relative energy shortage.
Discussion of analysis of the results
The above-mentioned distribution factors (Table 2) together with the knowledge of the dependence of the power system on wind energy allow to easily estimate what event we are prepared to (and probability with which this event can occur) . Analyses shown in Table 2 show also what is the level of energy reserves that should be concerned in order to prepare for a certain event with probability X. Such analyzes will undoubtedly be needed in the future for connection with the EU-promoted policy of increasing DER share (at this point the addiction is 0, ie in SEE SCO automation is not used, even when the generation from wind is 0).
We propose, in principle, 2 coefficients which describe the dependence of power system on wind generation. Both are connected to the coefficients of windmills utilization. First one tells – what percentage of windmill power has to be produced so SCO is not used in power system (without the use of stored energy) and the second one describe situation where power system works only to supply the most important loads (hospitals, traffic lights, telecommunication and industry which requires continuous supply)
It is also worth recalling the catastrophic events in Texas at the beginning of 2021 [29-32] related to the underestimation of the duration of extreme weather events and the emergence of a new factor that could lead to a blackout.
It should also be noted that regardless of our ability to predict the weather (energy production from windmills), reserves for the time when the energy generation by windmills has to be limited, must always be available , in one form or another. As DER penetration in the system increases, the scope of system balancing by thermal power plants will decrease. Thus, the investments in high dynamic power plants or energy storage are needed.
The shape formed in figure 8 is extremely important for future analyzes, especially in the area of the lower right corner – the highest energy production by windmills (i.e. the smallest cushion in thermal power plants and the greatest variability in others) in the case of unexpected weather event either it needs to be covered by high dynamic energy sources or SCO should be utilized.
Conclusion
The analysis done using figures 5 and 6 shows that onshore wind energy production in Poland is characterized by low level of basic production (median much lower than medium values) what indicates that we have frequent episodes of very low and high production. The shape in figure 6 shows this instability of the windmills energy generation. Even considering yearly periods we can observe high levels of variability of windmills energy production. This requires further analysis, especially when further windmills will be located also onshore and offshore in the Baltic sea.
Table 2. Cumulative distribution functions of energy shortage and time period of the event. Range – arbitral level of windmill usage below with power system experiences energy shortage, axes at (A) column – X axle: energy shortage [h x installed windmill power], Y axle: probability of events for which the lack of energy was greater than the value determined on the X axis; axes at (B) column – X axle: time period of event in hours, Y axle: probability that the time length of a certain event is shorter than x value.
.
Thus, the new data sets dependent on DER penetration in the power system will be different what force new approach to their analyses:
• The necessary level of basic energy production or reserve to keep system stability during the windmill production variability (with a certain probability)
• For a given basic production (fully controllable power stations) what production of windmills is necessary to keep a certain state of work of power supply system
• What is the probability of the event for which the operator want to prepare to be able to neutralize its influence on power system stability
• What amount of energy is necessary during the generation shortage by windmills (which happens with a certain probability) and what data are required to calculate reserves of stored energy or fuel necessary to cover energy needs if shortage event happens. All these calculations should be performed for the situation when will be no possibility of the restoration of energy reserves and fuel (second event just after first one)
Nowadays, not only the influence of DER sources production on energy price is visible but as the DER sources penetration in power system will grow, the limitation of energy availability from these types of sources become visible This can cause SCO (disconnection of a certain users) usage during severe energy shortages.
Takin under consideration analysis performed for Poland area we suggest to do similar analysis foe larger and varied terrain covering different areas in EU (UCPTE area). It is very unusual that there is no any attempts to cover all EU terrain (including off-shore locations) by analysis similar to performed in this paper especially in the case when penetration of DER sources is forced by EU officials.
In the available literature, one can also observe a strange indifference of the electrician community towards the problems of power system stability and unpredictability of RES generation. This problem was referred to in the works[33-39] and the amount of reserves needed for efficient balancing of the power system [40-42] in the articles published at that time in Przegląd Elektrotechniczny. The problems raised in the article may have an impact on the functioning of the economy and society – so far we have functioned in conditions of widespread availability of electricity and the only possible problem was its price. As the penetration of DER in the power system increases and with the expected development of energy storage technologies, a reduction in the reliability of supplies should be taken into account.
The motivation for the creation of this work was to check how large energy storage facilities should exist in Poland when switching to renewable energy.
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Dutta, “The Texas Freeze of February 2021: Event and Winterization Analysis Using Cost and Pricing Data,” 2021 IEEE Electrical Power and Energy Conference (EPEC), 2021, pp. 7-13, doi:10.1109/EPEC52095.2021.9621500 [33]Amar Bensaber, A., Benghanem, M., Guerouad, A., & Amar Bensaber, M. (2019). Power flow control and management of a Hybrid Power System. Przegląd Elektrotechniczny, 95. [34]Raczkowski, R., & Robak, S. (2021). System magazynowania energii elektrycznej jako środek poprawy elastyczności systemu elektroenergetycznego z dużym udziałem generacji OZE. Przegląd Elektrotechniczny, 97, 1-8. [35]Halinka, A., Rzepka, P., Szewczyk, M., & Szablicki, M. (2011). Przyłączanie farm wiatrowych-potrzeba nowego podejścia do sposobu funkcjonowania automatyki elektroenergetycznej sieci WN. Przegląd Elektrotechniczny, 87(9a), 218-221 [36]Gała, M. (2017). Praca turbin wiatrowych w systemie elektroenergetycznym oraz ich wpływ na jakość energii elektrycznej. Przegląd Elektrotechniczny, 93 [37]Jiang, Z., & Xie, K. (2012). Identification and effect analysis of the weak parts of large-scale wind energy conversion system using the reliability tracing technique. Przeglad Elektrotechniczny, 88(8), 192-196 [38]Suproniuk, M., Skibko, Z., & Stachno, A. (2019). Diagnostyka wybranych parametrów energii elektrycznej produkowanej w elektrowniach wiatrowych. Przegląd elektrotechniczny, 95(11) [39] Piekarz, M. The analysis of the wind generation impact on the power system stability PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 97 NR 11/2021 doi:10.15199/48.2021.11.27 [40]Kazanowski Robert, Dariusz Sztafrowski, ” System elektroenergetyczny oparty o odnawialne źródła energii – możliwości i bariery rozwoju,” Przegląd Elektrotechniczny, 02/2023 pp. 186 [41]DOWEJKO, J., JAWORSKI, J., Banaszak, S., Zeńczak, M., Małyszko, O. (2022). Wybór miejsca zainstalowania wodorowego bufora energetycznego w systemie elektroenergetycznym. Przegląd Elektrotechniczny. doi:10.15199/48.2022.10.51 [42]Kudria, S., Lezhniuk, P., Riepkin, O., & Rubanenko, O. Hydrogen technologies as a method of compensation for inequality of power generation by renewable energy sources. Przegląd Elektrotechniczny, ISSN, 0033-2097
Authors: dr. inż. Tomasz Sieńko, Politechnika Krakowska, Katedra Inżynierii Elektrycznej, ul. Warszawska 24, 31-155 Kraków, E-mail: tomasz.sienko@pk.edu.pl; dr hab. inż. Jerzy Szczepanik, Politechnika Krakowska, Katedra Inżynierii Elektrycznej, ul. Warszawska 24, 31-155 Kraków, E-mail: jerzy.szczepanik@pk.edu.pl.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 99 NR 9/2023. doi:10.15199/48.2023.09.08
Published by Dranetz Technologies, Inc., Case Study
A busy trading floor was experiencing difficulties with their six air handling units, which were fed from a single 480V, 400-amp service. The units were malfunctioning, stalling and at times were unable to start, causing the temperature in the control room to rise to unacceptable levels. A Dranetz Power Platform PP4300 was installed for a two-week period to evaluate the voltage and current of the 400-amp service feeding the handlers.
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Based on the collected data, we learned the following:
The top graph shows the voltage, which averages about 450V as opposed to the 480V expected. This resulted in a continual undervoltage situation, putting the system at a higher susceptibility for sags.
Three large sags, greater than 10% of nominal occurred (see purple arrows), and numerous sags at around 5% of nominal.
The lower graph shows the current, expected to be 400 amps. As you can see, on numerous occasions it rose above 400 A, and in several cases rose to 500 A.
The current rise can be correlated to voltage sags.
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In the above, we see that the voltage (in red) and the current (in blue) are both decreasing, indicating that the source of the sag can be found upstream of the monitoring point (PQ Rule #1). As a result of this survey, two of the air handlers were moved to a different service, eliminating an overloaded circuit condition and a source for voltage sags.
Published by Dranetz Technologies, Inc., Application Note
INTRODUCTION
Most of the world distributes power at 50Hz or 60Hz. However, there are certain specialized applications that distribute power at other frequencies. Applications such as aviation, naval, and others distribute power at 400Hz to improve system efficiencies. 400Hz applications can be a challenge for the end user since most products only measure at 50 and 60Hz. Many users think that all AC applications are the same and do not consider the fundamental frequency of the power system, assuming their instrument can handle this application, but few actually can. A misapplication of a 50/60Hz instrument in a 400Hz application will most likely result in inaccurate measurements, inconsistent instrument behavior, and loss of time and money as a result of surveys that do not collect useful data. 400Hz measurements require some enhancements to hardware and instrument firmware to accommodate the 400Hz fundamental frequency, and its associated measurement and synchronization requirements.
INSTRUMENTATION
Dranetz is one of the few Power Quality instrumentation manufacturers that offers products for 400Hz applications. The Dranetz HDPQ Xplorer 400, and its predecessor, the PowerXplorer PX5 400 fully function at 400Hz, as well as the traditional 50Hz and 60Hz ranges.
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The 400Hz versions of these advanced power quality, demand, and energy instruments offer the same powerful feature set as the traditional versions, but add 400Hz capabilities. They can meet virtually every application by accurately measuring traditional 50/60Hz, 400Hz circuits and DC systems. All of the Dranetz HDPQ Xplorer and PowerXplorer PX5 capabilities are available in their respective 400Hz versions, including high speed transient measurements.
AVIATION APPLICATION
400Hz power systems have the same concerns as traditional 50/60Hz systems, yet many users don’t know that an advanced tool is available to help them. Power quality, demand, and energy are important in any application, regardless of the power frequency. With the Dranetz 400Hz capable products, users can apply industry leading Dranetz monitoring technology to these more specialized applications. As an example, aviation power distribution reliability can involve three main components: the aircraft, jetway, and ground equipment/power. Any link in this chain that doesn’t perform to expectations can cause failures that may result in delays, lost productivity, and lost revenues. Therefore, it is important to have the tools available to quickly resolve any problems to reduce the economic impact of interruptions in service. The Dranetz HDPQ Xplorer 400 is an indispensable tool for not only troubleshooting problems, but when applied proactively, problems can be avoided altogether by understanding the dynamics of the power distribution system.
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As an example, the figure below shows a voltage sag displayed in our Dran-View 7 software that was recorded measuring the 400Hz aircraft supply from a jetway at a major east coast airport.
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Aviation power distribution can also go beyond 400Hz as aircraft power systems can distribute power at 50/60Hz, 28VDC, and other levels. Whether the application is troubleshooting onboard the aircraft or ground based equipment, it’s important to have the ability to measure the entire electrical environment without compromises. The Dranetz HDPQ Xplorer 400 has such capabilities, all in one easy-to-use instrument.
GATE POWER DEMAND AND ENERGY APPLICATION
An interesting new application is aircraft support energy surveys. In many cases, airport gates are rented or leased to airlines. The cost of the energy consumed by an airplane while parked at the gate is intended to be included in the fees charged to the airlines. However, in many cases, owners estimate energy usage and do not measure actual aircraft consumption for the purposes of adjusting fees accordingly. A Boeing 777 consumes more electricity than a regional jet, and owners need to make sure contracts and pricing are based upon actual usage and not estimates. The Dranetz HDPQ Xplorer 400 demand and energy monitoring capabilities can be used by airport authorities, ground maintenance teams, and airlines to easily conduct power consumption surveys to measure actual usage, making the information readily available to adjust billing/contracts accordingly. Such surveys can range from short term spot checks to longer duration monitoring surveys.
BAGGAGE CONVEYOR ENERGY EFFICIENCY APPLICATION
A major US airline planned to retrofit a portion of their baggage handling system at their Texas hub to include energy efficient 60Hz drives. The airline wanted to prove the effectiveness of this upgrade and to verify that the actual energy savings met those advertised by the supplier. They wanted a power monitoring tool to measure energy consumption before and after the upgrade that could also be their ‘go to’ tool for many other applications, including 400Hz aircraft power applications, facility and terminal power quality, demand, and energy monitoring applications.
The airline chose Dranetz, with the first application being to benchmark the energy consumption before the retrofit of the baggage handling system. After the retrofit, they did a comparable survey that positively verified that energy savings were as expected and met the supplier’s claims.
CONCLUSION
Aviation power systems have the same power quality, demand, and energy concerns as traditional 50/60Hz systems. However, due to the various power distribution methods (400Hz, 50/60Hz, DC), specialized instrumentation is required to accurately measure in this environment. The Dranetz HDPQ Xplorer 400 was specifically designed for such applications, yet retains traditional 50/60Hz capabilities, making it an indispensable tool for use in any power system in any airport or aircraft environment.
Dranetz HDPQ Xplorer 400 Aviation Applications
• Power quality troubleshooting • Real-time 50/60 or 400 Hz monitoring • Power system performance testing • Preventive or just-in-time maintenance • Testing of AC/DC systems • Power consumption, billing and allocation • Compliance with Mil std 1399 testing
Published by Dranetz Technologies, Inc., Case Study
Willows Swim Club is a private, member-owned swimming and recreational facility located in central New Jersey. The swim club provides kitchen space to a local restaurant to operate a food concession. Being offered as a service to its membership, there is presently no charge for rent or utilities. However, given the current economic climate the swim club is considering charging the concession operator next season to be reimbursed for the utility costs incurred operating the concession area. The primary objective of this energy audit was to measure the electrical demand and energy consumption of the concession area to determine demand and usage during an average summer peak operating period, representing worst case Demand and Energy usage. This information will be used to determine reasonable utility charges when negotiating the contract for next season.
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The swim club is seasonal business operating during the summer months from Memorial Day to Labor Day each year. The concession operates 7 days a week from about 11am to 7pm each day. Certain days have extended hours due to demand and other factors but days can also be shortened due to inclement weather. Swimming pools are outdoors so the facility may close or limit operating hours due to rain or cool temperatures. The concession area electrical loads are primarily comprised of cyclical loads such as refrigerators, freezers and room air conditioners but constant loads such as overhead lighting exist.
A two week energy survey using the Dranetz Energy Platform EP1 was conducted from July 14, 2009 to July 28, 2009. The EP1 was connected to the dedicated 120V split phase sub panel feeding the concession area. Two model TR2550, 100A CT’s were used for current measurements.
Below is the demand profile recording during the survey with the Peak Demand overlaid. Data is show in DranView 6 software:
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Survey results indicated the following:
Peak demand measured during the period was 7.353 KW.
Total electricity consumed was 1375.6 KWh
The survey ran for 13.95 days with an average consumption of about 98.5 KWh per day
General observations
The demand was cyclical and peaked during mid day hours as expected.
More consistent demand profile was observed during the first week of the survey vs. the second week.
Local weather data during the monitoring period was obtained from the Internet and compared to the demand profile. The comparison indicated rain and poor weather conditions corresponded with the reduction and inconstancy of the demand measured during the second week of the survey. Since such weather is consistent with a normal summer in the area this demand data should be included in the overall 2 weeks results being considered normal.
Electricity Cost Estimation
The local utility is Public Service Electric & Gas (PSE&G) Company. New Jersey, being a deregulated state provides consumers the option to purchase electricity supply from an independent provider other than the delivery company. The facility owner chose PSE&G as the electricity supplier so both Delivery and Supply charges come from the one utility. Even though the swim club is a comparatively small consumer of electricity the utility bill is more complex than a residential bill. The utility rate structure applied is PSE&G’s GLP, General Lighting & Power rate. Also being the electricity supplier, PSE&G charges their BGS, Basic Generation Service rate structure for supply charges. The resultant utility bill demonstrates the complexity of billing in a deregulated energy environment and also the importance of understanding your electrical usage in order to manage and reduce your utility costs:
Delivery:
Service charge: $4.27
Distribution charges
Annual Demand @ $3.9202/KW
Summer Demand @ $7.2755/KW
KWh charges @ $0.0145/KWh
Societal Benefits (recovery of costs incurred achieving government policy) @ $0.0075/KWh
This is a complex calculation that represents the customers share of the overall peak load assigned to the utilities transmission zone.
BGS Energy (tiered usage per KWh)@ $0.1089/KWh (first tier <9755 KWh)
Being such a complex rate structure with many billing factors, it can be difficult to relate some of these costs to actual usage by the concession. Since the objective is to estimate worst utility costs, a fair method was found with the following assumptions taken into account:
The operation of the swim club during the 2 week survey was deemed typical and representative an average two week period during peak summer months.
Actual usage results will be used in the estimated charges opposed to determining a percentage of the concession usage vs. the overall bill.
Service charge will be 100% paid by the owner.
BGS capacity charge will be ignored since there is no fair way to determine the concessions portion of these charges. The owner will bear this charge.
The various KW Demand and KWh Energy utility charges can be consolidated into one charge for KW and KWh portions of the estimate:
Consolidated KW Demand charge is: $11.1957
Consolidated KWh Energy charge is: $0.1413
Demand and Energy Summary Report
Site: Willows Concession final Measured from 07/14/2009 11:12:09.0 to 07/29/2009 02:37:01.0 BILLING DAY OF MONTH: 1
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Time of Use Billing
Site: Willows Concession final
Measured from 07/14/2009 11:12:09.0 to 07/29/2009 02:37:01.0 BILLING DAY OF MONTH: 1
Published by Dranetz Technologies, Inc., Case Study
With the proliferation of Compact Florescent (CF) lighting, we thought it would be interesting to do a side by side comparison to an incandescent bulb using our new Energy Platform EP1. Not only is it a great demonstration of the capabilities of the EP1 but it’s also an opportunity to explore the energy savings advantages while also looking at the potential effects on power quality.
Energy Star (www.energystar.gov) promotes the energy savings of CF lighting verses traditional incandescent bulbs. The industry claims a 75% reduction in energy usage of CF bulbs vs. incandescent. Is this true? To find out we used an EP1 to conduct a simple test to validate this claim. We randomly selected a 60W incandescent bulb and the equivalent (light output) 13W compact florescent. The CF bulb was connected to Channel A of the instrument while the incandescent was connected to channel B. Current was measured using two of our TR2501, 1A CT’s.
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As you can see from the EP1 screen capture above, the CF bulb does indeed consume 75% less energy than the equivalent incandescent. What about the power quality effects? Let’s look at the waveshapes…
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From the DranView screen shot above you can see that unlike the linear resistive load of the incandescent bulb, the electronic ballast in the CF is a non linear load. Like a computer power supply, it’s rich in harmonics. There are other PQ concerns such as start up currents but we’ll focus on the harmonics since it seems to be getting the most concern.
The CF current looks quite bad with a very high current THD of about 107%. However, when taking a closer look the actual current levels are quite small. The predominant harmonic is the 3rd at about 80ma.
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Will these low level harmonics cause any problems? If you Google something like “compact fluorescent thd” you’ll find numerous results on this topic. CF manufacturers claim no significant impact from such harmonics. Utilities are keeping a close eye on this issue as the proliferation of compact fluorescents continues to see if THD levels increase on their systems. One thing is clear; it will be the cumulative effects of a large number of such bulbs that would ultimately cause an issue. Even though 100+% current THD sounds bad, at such low current levels one bulb rich in harmonics clearly isn’t a problem. What happens when each house has 10, 20 or more of such bulbs operating simultaneously? That remains to be seen, hence the utilities concerns. However, as consumers, saving 75% in energy costs is the overriding factor!
Published by Dranetz Technologies, Inc., Case Study
The customer was experiencing repeated failures electronic controls and instruments in a new glass factory. The factory had been imported from a 60hz country to a 50hz country. In this case, the new factory capacity was reduced by 50% or more do the frequent shut downs and stoppages. The cost of repair of the electronics was a small part of the total cost of each failure. “You don’t just turn a glass factory off and on”. The customer had attempted identify the source of the problem with voltmeters and simple power monitors. They were not able to capture the damaging voltage variation. The graph from the meter showed no significant voltage variation. See Graph 1 for example of line to line and line to neutral RMS values show no high voltage variations.
Graph 1 – Example of line to line and line to neutral RMS values
We installed a Dranetz PG4400 Power Quality Monitor at the 400 volt buss level. The results are shown in graph 2. Notice the peak voltage graph clearly shows multiple high voltage transients recorded by the unique voltage peak recording capability of the PG4400. Graph 3 shows the detail of the voltage transient. In order to find the source of the transient, we installed additional Dranetz PX-5 and PG4400 monitors at the source voltage of 10Kv.
Graph 2 – Peak voltage, and multiple high voltage transients
Graph 3 – Detail of the voltage transient
Based on this evidence we determined the voltage transient had multiple sources. One source was from the 10KV voltage level as shown by these graphs. The second source was investigated in a separate project.
Published by Dranetz Technologies, Inc., Application Note
EXECUTIVE SUMMARY
As solar photovoltaic (PV) systems become more prevalent in commercial and industrial environments, the emphasis on financial return and concise performance expectations has rightly intensified. However, one often-overlooked aspect of solar deployment is the comprehensive evaluation of power quality (PQ)—both before and after installation. By identifying existing grid or facility issues and evaluating the impact of PV systems on power quality, stakeholders can protect infrastructure, enhance performance, and uphold warranty and compliance standards. This white paper explores the strategic benefits of PQ assessments in solar installations, drawing insight from field operations and test data, such as those captured during site evaluation in Xalapa, MX.
UNDERSTANDING POWER QUALITY IN THE CONTEXT OF SOLAR
Power quality refers to the stability and purity of the electrical power supplied to a facility or distributed across a grid. It encompasses factors such as voltage regulation, harmonics, flicker, and transients. These all can degrade equipment performance or signal systemic issues.
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IEEE 1547, a widely adopted standard for interconnecting Distributed Energy Resources (DERs) with electric power systems, must be taken into consideration, and all PV systems must comply with it. A recent amendment, IEEE 1547.1 – 2020 defines conformance testing and requires that IEC 61000-4-30 Class A compliant PQ meters are used for voltage and current distortion, and other evaluations.
PV systems, while beneficial, can interact with the grid in complex ways. Inverters and interconnection points may introduce harmonics or voltage fluctuations. Additionally, grid conditions that exist outside of expected parameters can prevent inverters from operating, negatively impacting PV system production and potentially damaging expensive componentry.
Without prior and post-installation PQ testing it can be difficult to identify the source of PQ problems as being caused by the PV system, previously existing on the grid, or an interaction between the two systems. Also, anomalies may go unnoticed, potentially leading to preventable and costly system inefficiencies or failures.
REAL-WORLD INSIGHT: LESSONS FROM THE XALAPA SITE
A recent evaluation was performed at a rooftop pergola mounted PV array in Xalapa, Mexico. The site can generate 16kW AC / 16.35kW DC with two single phase inverters of 10kW and 6kW.
The purpose of the evaluation was to conduct a ‘health check’ and demonstrate the benefits of a PQ study in a PV system. The field engineers utilized both PV and PQ instruments to evaluate the performance of the system and interaction with the grid. The instrumentation used was:
● Seaward PV210 IV-curve tracer, a tool specifically designed to verify that solar modules are performing at a consistent level ● Dranetz HDPQ Xplorer Plus, an IEC 61000-4-30 Class A, portable PQ instrument
OBSERVATIONS AND FINDINGS
PV Performance Issue Identified
IV-curve tracing is a method of testing solar modules and arrays to assess their performance and identify potential problems. It involves plotting the current (I) and voltage (V) relationship of the module. In this case, I-V curve trace data collected by the Seaward PV210 revealed an imperfect (defect) for one of the three solar strings tested. The root cause, which could be a problem like soiling, shading, or hardware failure, required further investigation to pin down. Without this test data, however, the defect may have gone unnoticed, potentially leading to long-term performance issues.
PQ Harmonics Issue Identified
Using the HDPQ meter, the site engineers were able to detect that harmonics on the inverter AC outputs were very high and well beyond the recommended limits of IEEE 519. As shown in Figure 1 below, Vthd was about 60% and there were significant even order harmonics which typically do not exist in most power systems. Harmonics can cause overheating in inductive devices such as transformers, resonances, high neutral currents, and overvoltage issues, and ultimately can cause system shutdown. Further investigations needed to be carried out to determine the source of the harmonics.
Figure 1. High harmonic content and significant even harmonics on the inverter AC output.
As shown in Figure 2, the DC input to the inverters has an approximately 11V oscillating at 120Hz. This is twice the 60Hz fundamental frequency and most likely due to the significant second order harmonics (120Hz) as described above.
Figure 2. Inverter AC output (top) and DC input (bottom). DC input oscillates at twice the grid frequency.
STAY OUT OF TROUBLE WITH THESE BEST PRACTICES
Conduct PQ testing pre- and post-installation. PQ testing before the PV system’s commissioning can provide a baseline to identify legacy issues, insulating the PV installer from preexisting issues. Post-commissioning tests can then isolate any PQ disruptions introduced by the PV installation itself.
This two-phase approach ensures grid readiness and verifies that PV integration has not degraded power quality. For the Xalapa site, it is difficult to determine if the harmonics problem existed before the PV system went online, or the PV system is the source.
Implement annual PQ and electrical health audits.
By testing power quality at least annually, project teams can assign responsibility for faults, validate performance, and ensure long-term reliability.
Correlate system performance with PQ metrics.
Make it part of your system maintenance process to conduct PQ tests and correlate PQ metrics to system performance. This helps you spot issues before they become problems and provides deeper insight into potential degradation and operational health.
Incorporate PQ results into acceptance and warranty protocols.
Doing so increases accountability across all stakeholders, from suppliers to installers, and site owners.
Spec in and install a fixed, permanent PQ meter.
While more expensive, a permanent PQ meter installation will provide far more data, on a consistent basis, into PQ events. Moreover, this may be the best option for problematic sites.
Compile warranty and compliance evidence
Creating a paper trail of electrical performance test data goes a long way to supporting warranty claims. In addition, it satisfies regulatory requirements.
THE BOTTOM LINE
As demonstrated in the Xalapa project, a structured approach to PQ testing that incorporates power quality evaluation into the lifecycle of solar installations can keep equipment up and running longer, and more importantly, keeps you out of trouble.
Power Quality Blog 