Peak Shaving Strategy Research Articles

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

Optimization tool for operating isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts

This paper presents a computational tool based on a genetic algorithm and artificial neural network for optimizing the operation of isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts obtained with quantile random forests. The optimization tool was conceived to be flexible, i.e., it can be used to operate isolated power systems with multiple configurations of diesel generator sets (DGS), to work with a reduced number of input data, and to be as simple as possible to be used. The optimization relies on combining valley-filling and peak-shaving strategies using battery energy storage systems while considering the combined forecast of demand and photovoltaic (PV) generation. The tool also simulates the behavior of the DGS to define the optimum arrangement of diesel generators considering the variability of both demand and PV generation. The output consists of hourly values of energy storage power dispatch, DGS arrangement, and, if necessary, load shedding and/or PV curtailment. The algorithm that implements the optimization tool, which is currently in the phase of field-test in the isolated diesel-photovoltaic-battery hybrid power system of Fernando de Noronha, Brazil, demonstrated a good performance in computer simulations validated with real measured data.

A comparative analysis of charging strategies for battery electric buses in wholesale electricity and ancillary services markets

The application of smart charging to battery electric buses can provide opportunities for bus operators to reduce the operational costs of their bus fleet. This research aims to create insight into the impact of different charging strategies for battery electric bus fleets on charging costs and the grid load. It proposes a novel framework to model the charging process of battery electric buses for different charging strategies: charging-on-arrival, peak-shaving, day-ahead market optimization with and without vehicle-to-grid (V2G) functions, including the provision of Frequency Containment Reserves (FCR) and automatic Frequency Restoration Reserves (aFRR) for system balancing in ancillary services markets. Model simulations are conducted to compare the charging costs and grid impact of different charging strategies, using three depots of bus operator Qbuzz in the Netherlands as a case study. Results indicate that the application of smart charging algorithms can considerably reduce charging costs for bus operators. Application of the peak-shaving strategy was found to reduce charging costs by 23–32% compared to the reference case of charging-on-arrival. Charging costs can be further reduced by 6–11% when considering day-ahead market optimization. Participation in ancillary services markets for system balancing is economically attractive for bus operators, particularly in the aFRR market, characterized by a cost reduction potential of 90–¿100% compared to the charging-on-arrival strategy. The grid impact analysis indicates that charging-on-arrival can result in high charging demand peaks, which can be drastically reduced by the application of peak-shaving or day-ahead market optimization charging strategies. However, the provision of aFRR and FCR using the battery electric bus charging process can have a severe impact on the local grid in terms of high peak demand.

Peak shaving strategy for load reduction of wind turbines based on model predictive control

The protection control system determines the operation safety of wind turbines. The peak shaving controller can protect wind turbines by mitigating the thrust peak near the rated wind speed. However, the traditional controller is designed in the form of a look-up table, which leads to limitations in stability and regulation capability. Therefore, an improved strategy based on model predictive control is proposed to enhance the performance of peak shaving regulation. During each control period, the allowable pitch range is improved by solving the optimization problem in real-time. Firstly, the traditional peak shaving controller is introduced and analyzed in detail. Then, a peak shaving strategy based on model predictive control is put forward for thrust peak protection by providing additional pitch action. The controller is established in a closed-loop control framework by solving an open-loop optimization problem. Finally, systematic simulations are implemented based on a high fidelity model in Bladed. As a result, it is demonstrated that the strategy can reduce the thrust peak to the safe range at the expense of acceptable output power. In addition, the proposed controller can relieve the alternating load in the peak shaving range and mitigate fatigue loads of key components.

A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements

The objective of the traditional dispatching model of multi-energy systems is to seek the most economical scheduling mode, while ignoring the operating risk caused by inaccurate predictions of wind and PV power. Given this, this paper proposes a framework for the short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system (HTWPS). First, a quantile regression method and multivariate Gaussian distribution are adopted to generate wind-PV scenarios. Then, a short-term risk and economic dispatching model of the HTWPS considering spinning reserve (SR) requirements is established to minimize the operating risk and cost. Finally, a synchronic peak-shaving strategy for hydropower stations is developed to determine reasonable hydropower generation plans in model solving. The proposed framework is applied to China’s Qinghai grid. The results show that: (1) As comprehensive utilization flow rises, the cost and risk drop sharply in the dry season, but almost keep unchanged in the wet season constrained by hydropower SR boundary. (2) The power abandonment risk in the dry season is high due to the low hydropower generation capacity induced by limited runoff. The power abandonment and shortage risk are mainly subject to the hydropower SR boundary in the wet season. (3) Compared with the operating mode with hydropower SR alone, the comprehensive risk decreases by 10.6%, and the comprehensive cost rises by 5.8% under the operating mode with hydro and thermal power SR (HTSR) in the dry season. The comprehensive risk decreases by 51.4%, and the comprehensive cost increases by 172.8% under HTSR in the wet season.

Peak Shaving for Electric Vehicle Charging Infrastructure—A Case Study in a Parking Garage in Uppsala, Sweden

The need for a more flexible usage of power is increasing due to the electrification of new sectors in society combined with larger amounts of integrated intermittent electricity production in the power system. Among other cities, Uppsala in Sweden is undergoing an accelerated transition of its vehicle fleet from fossil combustion engines to electrical vehicles. To meet the requirements of the transforming mobility infrastructure, Uppsala municipality has, in collaboration with Uppsala University, built a full-scale commercial electrical vehicle parking garage equipped with a battery storage and photovoltaic system. This paper presents the current hardware topology of the parking garage, a neural network for day-ahead predictions of the parking garage’s load profile, and a simulation model in MATLAB using rule-based peak shaving control. The created neural network was trained on data from 2021 and its performance was evaluated using data from 2022. The performance of the rule-based peak shaving control was evaluated using the predicted load demand and photovoltaic data collected for the parking garage. The aim of this paper is to test a prediction model and peak shaving strategy that could be implemented in practice on-site at the parking garage. The created neural network has a linear regression index of 0.61, which proved to yield a satisfying result when used in the rule-based peak shaving control with the parking garage’s 60 kW/137 kWh battery system. The peak shaving model was able to reduce the highest load demand peak of 117 kW by 38.6% using the forecast of a neural network.

Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment

Although considered to be one of the most valuable alternative energy sources, the utilization of renewable energy still confronts challenges such as low conversion efficiency and large transient fluctuations. To address this issue, we propose a renewable hybrid system driven by solar and radiative cooling, develop advanced energy management and peak shaving strategies. After establishing and validating the mathematical model, the renewable energy recovery, subsystem variation law analysis, and hybrid system performance calculation are computed. Three-objective optimization under four cases is implemented after understanding the change rules. Subsequently, the energy management and peak shaving are performed, as well as the positive contribution of the storage system to the renewable hybrid system is discussed. Based on the equivalent coefficient, the renewable hybrid system benefits are finally assessed. The numerical results conclude that the renewable hybrid system has excellent peak shaving characteristics, can fulfill dynamically changing energy demands. Its equivalent work, energy efficiency, and exergy efficiency reach 155.11 kW, 23.17%, and 70.61%, respectively, with additional emission reduction, carbon capture, and sewage treatment capacities of 131.69 kg/h, 131.82 kg/h, and 1.509 kg/s, can recover the cost within 4.29 years. It complies with sustainable development models, conforms to carbon neutrality requirements, as well as provides meaningful guidelines for renewable energy utilization.

Peak-shaving Strategies for LP Gas Cylinder Distribution based on IoT Data

Peak-shaving Strategies for LP Gas Cylinder Distribution based on IoT Data

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction).

Determination of the Electricity Storage Power and Capacity for Cooperation with the Microgrid Implementing the Peak Shaving Strategy in Selected Industrial Enterprises

The capacity limitations of distribution networks, the increasing demands for electricity installation in terms of electricity quality parameters, and the development of electromobility all increase the need to implement systems in order to stabilize and regulate loads for end users. Battery Energy Storage Systems (BESSs), that operate in internal microgrids of enterprises, allow smoothing of electrical power consumption profiles by actively reducing peak demand power. By using BESSs to implement a peak shaving strategy to reduce the peak power consumption of a company in an external network, energy efficiency can be improved by reducing contractual capacity. The aim of this study is to determine the conditions for the use of energy storage, in order to implement a peak shaving strategy for which the installation of the enterprise microgrid is economically efficient. The analysis of the operating conditions of the BESS should take into account the size of the energy storage, the characteristics of the demand profile for the demand systems, the charges related to electricity, and the costs of electricity storage. In the study, the size of the energy storage was related to the power and electrical capacity of the BESS that was used to implement the peak shaving strategy. The article presents the results of research on the method for determining an effective capacity and power of BESSs for enterprise microgrid systems. The technical and cost limitations of the actual microgrid system, which affected the decrease in economic efficiency of the peak shaving strategy in the company research, were taken into account. The simulations of the operation of the electricity storage system, based on real data of the demand of production companies, were based on the rules and market conditions in Poland.

An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring

Traditional electric energy systems are experiencing a major revolution and the main drivers of this revolution are green transition and digitalization. In this paper, an advanced system-level EMS is proposed for residential AC/DC microgrids (MGs) by taking advantage of the innovations offered by digitalization. The proposed EMS supports green transition as it is designed for an MG that includes renewable energy sources (RESs), batteries, and electric vehicles. In addition, the electricity consumption behaviors of residential users have been automatically extracted to create a more flexible MG. Deep learning-supported Non-intrusive load monitoring (NILM) algorithm is deployed to analyze and disaggregate the aggregated consumption signal of each household in the MG. A two-level EMS is designed that coordinates both households and MG components using optimization, forecasting, and NILM modules. The proposed system-level EMS has been tested in a laboratory environment in real-time. Experiments are performed considering different optimization periods and the effectiveness of the proposed EMS has been shown for different optimization horizons. Compared to a peak shaving strategy as a benchmark, the proposed EMS for 24-hour horizon provides a 12.36% reduction in the residential MG daily operation cost.

A novel design of cold energy cascade utilization with advanced peak-shaving strategy integrated liquid air energy storage

For the sake of alleviating the pressure of energy and environment, compressed natural gas vehicle has become an important development direction of the automobile industry in the world with its advantages of low cost and restraining greenhouse effect. However, the operation of liquid-compressed natural gas station will inevitably produce boil off gas and gasoline vapor, which will cause great pollution to the environment if discharged into the atmosphere. Furthermore, in the face of increasing number of liquid-compressed natural gas stations, how to efficiently recover the cold energy released in the process of liquid natural gas regasification is a huge challenge. In this paper, a novel design integrating boil off gas and gasoline vapor recovery, liquid air energy storage and parallel Rankine cycle is proposed to recover cold energy of LNG in different temperature ranges for liquid-compressed natural gas station. The optimal ratio of binary mixtures are selected by genetic algorithm for parallel Rankine cycle, and the result shows that compared with pure working fluid, the total net power output of binary mixtures are increased by 6.62% to 3028.01 kW. Besides, an advanced peak-shaving strategy is proposed to satisfy the electricity demand of users at different time periods. The result of economic analysis exhibits that the payback period of the system is 5.24 years. This novel design not only provides a new idea for the recovery of boil off gas and gasoline vapor, but also complements the blank in the filed of cold energy utilization of liquid-compressed natural gas station.

A modeling framework to examine photovoltaic rooftop peak shaving with varying roof availability: A case of office building in Hong Kong

The United Nations Sustainable Development Goal 7 (UNSDG 7) “Affordable and Clean Energy” strives to increase renewable energy share in energy consumption. Building-integrated photovoltaics generate renewable energy and offset fossil fuels carbon emissions. As roof space availability varies among different buildings, the potential to utilize rooftops as photovoltaic systems is uncertain. This study's aim is justified by the need to develop a systematic modeling framework to examine photovoltaic rooftops with varying roof availability and to propose peak shaving strategies for reducing peak load and carbon emissions. The framework incorporates computational simulations to model the building power demand and evaluate different peak shaving strategies. By identifying building peak loads, peak shaving offsets part of the loads with photovoltaic electricity. This framework can be applied globally given suitable data, and is demonstrated on a 10-story reference office building with photovoltaic installations occupying 10%, 30%, and 50% of the roof area. In this demonstration, 9 peak shaving strategy options are considered for covering a varying number of peaks that last for different durations on weekdays. Peak load reduction and carbon emission saving in each option are assessed, optimal peak shaving options are identified according to seasonal changes and available area for photovoltaics. For example, when 50% of the roof's area is available, a full-office-hour strategy is proposed in summer, saving approximately 595 kg CO2 per weekday. In winter, solar generation is reduced. A proposed strategy covering full-office-hour except lunch hours yielded an estimated 271 kg CO2 saving per weekday. Based on these findings, policies are recommended to encourage utilization of existing rooftops, construct photovoltaic-ready roofs on new buildings, and guide urban planning to avoid excessive shading.

Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems

Formulating an optimal operation strategy is a real challenge in the actual operation stage of a hybrid system. This study presented a research framework for determining the short-term optimal operation strategy for cascade hydro-photovoltaic hybrid system. First, a method was presented for describing the cascade hydro-photovoltaic hybrid system net load utilizing the daily average net load and fluctuation coefficient. Second, a model was established for the short-term optimal operation of the cascade hydro-photovoltaic hybrid system. Then, a synchronous peak-shaving strategy of cascade hydropower stations was proposed based on the fluctuation coefficient. Finally, the framework was applied to a case study in the Qinghai Province, China. The results showed an obvious competitive relationship between the two objectives. In the hybrid system, the peak-shaving task is mainly carried out by a daily regulation hydropower station, while its base-load task is jointly carried out by runoff and daily regulation hydropower stations. When the average daily runoff is less than 30% of the hydropower rated flow, the cascade hydro-photovoltaic hybrid system should bear single-peak load on sunny, cloudy, and dusty days, otherwise, it should bear double-peak load. It should bear double-peak load under different daily average runoffs on rainy and snowy days. After the cascade hydropower relative position was interchanged, the cascade hydropower overall peak-shaving capability decreased. Thus, the above results prove the feasibility of the research framework, and it provides a reference for the short-term operation of cascade hydro-photovoltaic hybrid system.

Methodology and Simulation of Electrical Grid Peak Shaving Strategy based on Photovoltaic and Storage Optimization: an Italian Residential Sector Case Study

The European Union has set some ambitious targets to reach the goal of net-zero greenhouse gasses by 2050. The outlined scenarios provide the use of Renewable Energy Sources (RES) on a large-scale, but to do so, different kinds of actions must be taken, because the ample amount of non-programmable electricity sources may cause grid management problems and a mismatch in the energy supply and demand. The vast increase of the Italian power demand, which typically occurs in the evening, necessarily requires a rapid increase in thermoelectric power generation. A possible solution to avoid this phenomenon is the optimization of photovoltaic production and storage and, simultaneously, the minimization of the Life-Cycle impact of these systems on the environment. This work aims to identify a methodology that supports the analysis and design of a production, self-consumption and storage system, which services a residential user aggregate, in order to reach an electric power demand optimization. In particular, the target is to obtain a Peak Shaving of the electrical demand power curve, by setting a limit on the maximum absorption of power from the grid, and supplying the rest of the user’s power needs through an electrical energy storage system, charged from the photovoltaic plant during the daily overproduction time. To do so, 14 dwelling power consumptions have been aggregated and analysed, starting from a data monitoring that occurred in January and June of 2019. The energy consumptions considered, are in line with the Italian average ones. The Peak Shaving strategy effectiveness has been evaluated by using a percentage parameter, that represents the number of power absorption peaks from the grid, avoided thanks to the storage system. In this study, some optimal system plant settings, in terms of maximum power absorption from the grid, photovoltaic nominal peak power and electrical storage capacity, are investigated to reach the set goals, and some solutions are presented, in light of the needs of the public grid where the system operates.

Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. In this work, we analyze a number of applicable tools and find that most of them do not allow coupling of infrastructures, oversimplify the grid model or are based on inaccessible source code. We introduce the open source piping grid simulation tool pandapipes that—in interaction with pandapower—addresses three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes, we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then, we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as an interaction of a local electricity and district heating grid in a small neighborhood. The second case study analyzes the potential of a power-to-gas device to provide flexibility in a power grid while considering gas grid constraints. These cases show the importance of performing coupled simulations for the design and analysis of future energy infrastructures, as well as why the software should fulfill the three criteria.

Optimal design and operation of an urban energy system applied to the Fiera Del Levante exhibition centre

To move from centralised fossil fuel-based energy systems, synergies between distributed renewable generation, storage and demand-side strategies can be exploited to lower environmental impact and costs. This paper proposes an optimisation model for the techno-economic assessment of energy management strategies with a short-term investment horizon aimed at business managers and decision-makers in the commercial sector. The main novelty is the selection of a combination of on-site technologies and peak shaving strategies to minimise energy costs under time-of-use electricity tariffs, and the adaptation of a general methodology for a specific socio-technical context under seasonal loads. The “Fiera del Levante” exhibition centre in the city of Bari is selected due to the high seasonality of its electricity demand. The optimal solution uses a combined system with photovoltaics, diesel-fired and gas-fired combined-heat-and-power, including part-load operation and electric storage. The cost minimisation scenario reports up to 20% cost savings and 35% carbon emission savings with a 1MWp photovoltaic plant, compared to the baseline. This presents a five-year return on investment of 75%, and levelized cost of energy of €0.14 kWh−1. When coupled with a lithium-ion battery, solar energy brings up to 60% carbon emission savings through load shifting strategies, though this reduces the five-year return on investment by 9%. This hybrid setup is not financially competitive in the Italian retail market, but a hypothetical 25% rise of the grid import prices would make it economically viable. The proposed model is flexible and can be adapted to commercial end-users, providing decision-support in urban energy systems under local conditions.

Air-Conditioner Group Power Control Optimization for PV integrated Micro-grid Peak-shaving

<p style='text-indent:20px;'>Heating, Ventilation, and Air-Condition (HVAC) systems are considered to be one of the essential applications for modern human life comfort. Due to global warming and population growth, the demand for such HVAC applications will continue to increase, especially in arid areas countries like the Arabian Gulf region. HVAC systems' energy consumption is very high and accounts for up to 70% of the total load consumption in some rapidly growing GCC countries such as Qatar. Additionally, the local extremely hot weather conditions usually lead to typical power demand peak issues that require adequate mitigation measures to ensure grid stability. In this paper, a novel control scheme for a combined group of Air-Conditioners is proposed as a peak-shaving strategy to address high power demand issues for Photo-Voltaic(PV)-integrated micro-grid applications. Using the local daily ambient temperature as input, the AC group control optimization is formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. Under an acceptable range of indoor temperatures, the units in the same AC group are coordinately controlled to generate desired power consumption performance that is capable of shaving load peaks for both power consumption and PV generation. Finally, various simulations are performed that demonstrate the effectiveness of the proposed control strategy.</p>

Evaluation and Simulation on the Reserve Capacity of Thermal Storage Electric Boiler Clusters

After providing a better service for nearby heating loads, a thermal storage electric boiler system can use the heat storage tank to participate in the peak-shaving and reserve services of the power grid. Current researches focus on its peak-shaving capacity, but ignores the use of its reserve capacity, which is important to the random fluctuations of renewable energy sources. Facing the actual analyzing demands on power system adequacy, this paper starts with the discussion of typical operation modes, and builds the powersystem- adequacy model of a thermal-storage electric boiler based heating system. Based on the presented model, the concept of feasible domain for heat charging and discharging is proposed. By implementing the concept, the quantitative evaluation method to calculate the reserve capacity of a thermal storage electric boiler system is proposed. The verification of the method is also given. Based on the planning data of an actual power grid, the short-term reserve capacity as well as the effect on promoting renewable energy consumption is estimated according to the given size of the provincial thermal storage electric boiler clusters. Finally, the influences of different choices of peak-shaving strategies, heat storage tank capacity on its reserve capacity are simulated and analyzed.

Power grid peak shaving strategies based on electric vehicles and thermal storage electric boilers

Due to the rapid progress of electrification and the rising accommodation of renewable energy, the peak-to-valley difference of power grids has been increasing, and the peak loading pressure of power grids has arised. It has become a trend to use controllable loads to participate in power grid peak shaving. At present, electric vehicles and thermal storage electric boilers, which are widely implemented in northern China, provide a reliable source for controllable loads. Based on the analysis of the operational characteristics of electric vehicles and thermal storage electric boilers, this paper studies the charge and discharge control strategies for auxiliary peak shaving. The Monte Carlo method is used to simulate the charging behavior of electric vehicle users. Under the premise of considering the constraints of electric vehicle charging and discharging power, battery capacity and the power consumption of the thermal storage electric boilers, the mathematical model is established with the goal of suppressing the daily load curve, and the particle swarm optimization algorithm is used to solve the optimization problem. The simulation results show that electric vehicles and thermal storage electric boilers are jointly involved in load balancing, and the daily load curve is significantly levelled compared with the original curve.