Peak Power Demand Research Articles

Short-term load forecasting using an Artificial Neural Network for Battery Energy Storage System

Load levelling that use recent spotlighted the Battery Energy Storage System (BESS) saves spare electricity or low cost electricity. The saved electricity can be used for peak power demand time. In this paper, load is predicted for purpose of operating the BESS effectively. By analysing pattern of industrial load, it is classified into workdays, Saturday and holidays. Then apply ANN on workdays load pattern from classified load patterns to predict load. To increase accuracy, number of neurons in hidden layer and learning data period are changed for estimation of load. After that, error rate was calculated for comparison and analysis.

Modeling and simulation of photovoltaic powered battery-supercapacitor hybrid energy storage system for electric vehicles

Energy storage is crucial for the powertrain of electric vehicles (EVs). Battery is a key energy storage device for EVs. However, higher cost and limited lifespan of batteries are their significant drawbacks. Therefore, to overcome these drawbacks and to meet the energy demands effectively, batteries and supercapacitors (SCs) are simultaneously employed in EVs. A solar photovoltaic (PV) powered battery-supercapacitor (SC) hybrid energy storage system has been proposed for the electric vehicles and its modeling and numerical simulation has been carried out in MATLAB Simulink. The SC is used to supply the peak power demand and to withstand strong charging or discharging current peaks. It also improved the battery pack's durability and extended its life. Different topologies of battery and SC have been explored and their capacity to manage the battery stress is assessed. The passive topology is found to be the most suitable for the proposed model due to its ease of implementation and absence of control scheme. A hybrid topology is used to share the power across batteries, supercapacitors and the PV system. In the proposed hybrid energy storage system, a sudden load on the battery is shifted towards the capacitor and thus, the battery heating is reduced, that ultimately improved the vehicle performance and reduced the charging time. In addition, the supercapacitor stored a lot of energy in a short period of time and delivered more power to the load without experiencing any life-span deterioration. The results indicated that employing a passive DC-DC converter and hybrid energy storage system (HESS) reduced the battery power by 52 %, while the passive HESS system reduced the motor current by 94 %. The supercapacitor also recovered 51 % more energy while starting and can offer peak power more efficiently than a battery.

Optimization of a Hybrid Off-Grid Solar PV—Hydro Power Systems for Rural Electrification in Cameroon

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods.

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%).

Cost Minimization for Charging Electric Bus Fleets

Recent attention for reduced carbon emissions has pushed transit authorities to adopt battery electric buses (BEBs). One challenge experienced by BEB users is extended charge times, which create logistical challenges and may force BEBs to charge when energy is more expensive. Furthermore, BEB charging leads to high power demands, which can significantly increase monthly power costs and may push the electrical infrastructure beyond its present capacity, requiring expensive upgrades. This work presents a novel method for minimizing the monthly cost of BEB charging while meeting bus route constraints. This method extends previous work by incorporating a more novel cost model, effects from uncontrolled loads, differences between daytime and overnight charging, and variable rate charging. A graph-based network-flow framework, represented by a mixed-integer linear program, encodes the charging action space, physical bus constraints, and battery state of the charge dynamics. The results for three scenarios are considered: uncontested charging, which uses equal numbers of buses and chargers; contested charging, which has more buses than chargers; and variable charge rates. Among other findings, we show that BEBs can be added to the fleet without raising the peak power demand for only the cost of the energy, suggesting that conversion to electrified transit is possible without upgrading power delivery infrastructure.

Design, development, integration and evaluation of hybrid fuel cell power systems for an unmanned water surface vehicle

When fuel cells are used to power mobile applications, such a vehicles, hybridization with batteries is normally required. Depending on the electronic coupling between the energy sources the power plants can have passive or active configurations. Hybrid fuel cell-battery power plants with active power control flow have some advantages. For example, they can decrease the total energy losses, while improving the fuel cell performance, extending its lifetime. Power plants with DC/DC converters show low specific energy ratios, but with a superior energy management. In the present research, the hybrid power plant for an unmanned aquatic surface vehicle (USV) based on a PEM fuel cell and a Li-ion battery is developed. Active (with DC–DC converters) or passive architectures are analyzed by numerical simulations and experimental tests. Good results are obtained for the active power plant, where the peak power demands are managed by the battery pack while the fuel cell power remains constant thanks to the DC-converter control. The study shows that a simple control algorithm (no optimal) can help to extend the USV autonomy above 12h in calm waters with a specific energy of 85.6Whkg-1.

Optimizing job shop scheduling with speed‐adjustable machines and peak power constraints: A mathematical model and heuristic solutions

AbstractThis paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power‐flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi‐objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small‐sized instances. We also proposed a multi‐objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade‐off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4–6% increase), while at the same time allowing for a small reduction in energy consumption.

Battery–integrated combined cooling, heating and power plant (CCHP) through NH3 – H2O absorption system in a hospital facility

Combined heat and power (CHP) systems have been employed in various applications for years. They are gaining increasing attention in the residential and small industrial sectors for their primary energy saving potential and CO 2 emissions reduction. Additionally, in specific applications such as the hospital sector, CHP plants can play a critical role in replacing fossil-fueled electric generators to supply electricity during local grid outages, thus enhancing hospital facilities’ energy efficiency, while also securing reliability and efficiency of operation. Hospitals have unique energy demand profiles, with high and constant demand (particularly heating and cooling demands), making them an ideal use-case for trigeneration systems. Combined, cooling, heating and power plants (CCHP) are capable to provide not only heating and electricity, but also cooling through the efficient exploitation of a single energy source, reducing Hospitals’ reliance on the local grid. In this context, this work aims at evaluating the potential of a battery-integrated CCHP plant through an innovative ammonia-water absorption system, whose energy analysis is based on data acquired from an Italian Hospital facility. The potential of integrating an experimental combined cooling and power production ammonia-water absorption system has been investigated starting from an optimized battery energy storage system (BESS) CHP plant configuration, studied for the same facility. A proper control strategy has been developed to maximize the cooling production when required from the end-user through CHP plant’s exhaust gases waste heat recovery. The energy analysis demonstrated the advantages of the CCHP-BESS plant over the optimal CHP-BESS configuration, with an 11.22% increase in primary energy saving, a 9.85% reduction in CO 2 emissions, and a 5.03% decrease in electric peak power demand.

Model predictive control for demand response in all-electric school buildings

This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.

Techno-Economic Analysis of the Energy Resilience Performance of Energy-Efficient Buildings in a Cold Climate and Participation in the Flexibility Market

Unexpected power outages and extreme weather encouraged research on energy-resilient buildings throughout the world. Resilient building research mainly focuses on hot weather rather than cold extremes. This study defines resilience terminologies based on the available literature and discusses the impact of energy efficiency on energy resilience performance in energy-efficient buildings due to abrupt power outages in an extremely cold climate. The assessment involves the case simulation of a multistory apartment located in southern Finland at design outdoor conditions (−26 °C) in IDA-ICE 4.8, a dynamic building simulation software, and its techno-economic assessment to ensure building resilience for up to 7 days of power outages. The assessment shows the efficient building envelope can enhance the time taken by the building to drop the indoor temperature to the threshold by approximately 15%. Additionally, the efficient heating system along with the building envelope can reduce the instantaneous power demand by up to 5.3 times, peak power demand by up to 3.5 times, and on average power consumption by 3.9 times. Similarly, the study finds that the total energy requirement during a blackout can be reduced by 4.1 times. The study concludes that enhanced building resilience is associated with energy-efficient parameters such as an efficient energy system and an efficient building envelope that has low thermal losses and high thermal inertia retention. The batteries contribute the maximum proportion to the overall retrofitting cost, and the proportion can go up to 70% in baseline configurations and 77% in efficient configurations of buildings. The analysis concludes that the required investment varies largely with the technologies involved and the combination of components of these energy systems. The assessment finds that the high investment costs associated with batteries and battery recharging costs are the main bottlenecks to feasible flexibility in market participation.

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

AbstractA hybrid method is proposed for electric‐vehicle (EV) fast charging station (FCS)‐based power electronics converters with energy‐storage‐systems (ESS) and renewable‐energy‐sources (RESs). The proposed approach is the combination of the fire hawk optimizer (FHO) and gradient boost decision tree (GBDT) algorithms; hence called as FHO‐GBDT approach. The key objective of the FHO‐GBDT approach is to lessen the peak power demand on the grid. The proposed method is incorporated into EV‐FCS with the capability of a mixture of RESs and energy‐storage‐systems. The capacities of energy‐storage aid in improving power‐demand by lessening the demand for peak power. The structure of the energy storage system minimizes the net cost of the DC micro‐grid (MG). The ESS is mostly composed of batteries, which are reused by EVs. The proposed approach and the ESS enable a decrease in obtaining the greatest amount of power possible from the power‐grid (PG). By then, the performance of the proposed approach is simulated in MATLAB, and it is compared to various existing methods. The simulation result shows that the proposed method offers more power than the existing methods.

Improved vµ-LMS-Controlled Single-Phase Grid Tied PV Powered Electric Vehicle Battery System with High Gain DC-DC Converter and Power Management

The increasing solar photovoltaic (PV) and EV charging applications create power quality issues at utility grid. It requires efficient control thus, in this paper, single-phase grid-tied PV powered electric vehicle battery (EVB) system is proposed with reliable control and power management. The presented work offers efficient EVB charging either from PV power or utility to handle PV impacts on the utility. During peak power demand, the proposed power management offers less burden on utility and allows EVB to feed increased load along with changing PV power. In work, a recent high-gain DC-DC boost converter topology is utilized thereby making smaller household PV arrays in grid-tied mode with DC-AC voltage source converter (VSC). Moreover, a new improved variable step-parameter ‘µ’ least mean square (IvµLMS) adaptive control is proposed and compared for efficient control of VSC with various nonlinear load and PV irradiance variations. The IvµLMS offers good convergence, less burden on controller and improved dynamic responses under various conditions. As per IEEE-519 standard, the PV power is fetched to grid at unity power factor (UPF) with low total harmonic distortion (THD). The proposed grid tied EVB system, and its controls are modelled in MATLAB/Simulink environment and test results are analyzed and validated on a laboratory prototype.

Data-driven online energy management framework for HVAC systems: An experimental study

The optimization of the operation of heating, ventilation, and air conditioning (HVAC) systems has gained considerable attention due to its energy conservation potential. Collecting various building operation data presents an opportunity to improve the efficiency of energy management and reduce consumption. In this paper, a data-driven online energy management framework is proposed for enhancing the effectiveness of HVAC systems and minimizing energy consumption. To streamline the modeling process and improve the accuracy of temperature estimation, our proposed framework adopts a symbolic regression approach to estimate building thermodynamics based on the collected data. It also formulates a model predictive control (MPC)-based HVAC scheduling strategy to optimize HVAC operations while maximizing thermal comfort and reducing energy consumption and peak power demand. We conducted on-site experiments to evaluate the proposed framework and demonstrated a notable reduction in total energy consumption in both HVAC cooling and heating operations: average reductions of 4.9% and 30.2%, and 49.3% and 73.9% for best cases in the on-site experiment under certain conditions. In the heating operation, the proposed data-driven HVAC management framework efficiently utilized indoor/outdoor heat generation to maintain comfortable temperature levels without activating the HVAC. Such effective utilization of heat sources shortened the HVAC runtimes and further reduced the total energy consumption. With the proposed framework, the average HVAC peak power demand fell by 25.8% and 35.1% during cooling and heating operations in the on-site experiment with certain scenarios. On-site experiment results demonstrated the proposed framework can efficiently suppress peak power demand, reduce energy consumption in both cooling and heating operation under certain conditions. The framework’s data-driven approach provides an effective means of optimizing HVAC operations, reducing energy consumption, and improving the thermal comfort of the building’s occupants.

Techno-Economic Assessment of the Viability of Commercial Solar PV System in Port Harcourt, Rivers State, Nigeria

Supermarkets in Port Harcourt (PH) city, Nigeria, predominantly rely on diesel electricity generation due to grid instability, leading to high electricity prices. Although solar photovoltaic (PV) systems have been proposed as an alternative, these supermarkets have yet to adopt them, mainly due to high investment costs and a lack of awareness of the long-term financial and environmental benefits. This paper examines the technical and economic practicality of a PV system for these supermarkets using the PVsyst software and a spreadsheet model. Solar resources showed that PH has a daily average solar radiation and temperature of 4.21 kWh/m2/day and 25.73 °C, respectively. Market Square, the supermarket with the highest peak power demand of 59.8 kW and a 561 kWh/day load profile, was chosen as a case study. A proposed PV system with a power capacity of 232 kW, battery storage capacity of 34,021 Ah, a charge controller size of 100 A/560 V, and an inverter with a power rating of 60 V/75 kW has been designed to meet the load demand. The economic analysis showed a $266,936 life cycle cost, $0.14 per kWh levelized cost of electricity (LCOE), a 4-year simple payback time, and a 20.5% internal rate of return (IRR). The PV system is feasible due to its positive net present value (NPV) of $165,322 and carbon savings of 582 tCO2/year.

A Hierarchical Control Strategy Based on Dual-Vector Model Predictive Current Control for Railway Energy Router

The multiport and multidirectional energy flow of railway energy routers (RERs) poses a significant challenge when integrating photovoltaic (PV) systems and energy storage systems (ESSs). To address this issue, this paper proposes an improved hierarchical control strategy for RERs with a reference signal generation layer and an inverter control layer. In the reference signal generation layer, a time-segmentation energy allocation strategy based on a state machine is proposed to manage the multidirectional energy flow in RERs resulting from PV systems and ESSs while minimizing peak power demand. In the inverter control layer, a dual-vector model predictive current control (MPCC) strategy is designed for back-to-back inverters. The dual-vector MPCC strategy eliminates the need for individual PWM blocks, thereby enhancing RER current-tracking accuracy and efficiency. The prominent advantage of the dual-vector MPCC strategy is its ability to achieve high current-tracking accuracy while minimizing active power losses. Simulations and hardware-in-the-loop experiments are conducted to validate the feasibility and effectiveness of the proposed method.

Performance and dynamics of California offshore wind alongside Western US onshore wind and solar power

We use statistical methods to evaluate the coincidence, variability, alignment, and predictability of California offshore wind power production with electricity demand and onshore renewable resources in the United States Western Interconnection region. We consider previously reported benefits, and evaluate possible advantages of deploying offshore wind as compared to onshore wind or solar power generation, such as coincidence with peak power demand and complementarity with other renewable power resources. Additionally, we use the Fast Fourier Transform and linear regression to assess the predictability of power with onshore wind and solar. The Pearson correlation reveals that onshore wind and solar are the most complementary renewable power generation resources, while there is no consistent correlation or anti-correlation between the offshore wind sites and the demand, solar, or onshore wind. Using a demand-based value metric, we find that the six potential offshore wind sites are more valuable than all other renewable resources during peak hours in the summer in California. Finally, the Fourier analysis reveals that offshore wind may be a highly variable resource.

A feasibility analysis of PV-based off-grid rural electrification for a pastoral settlement in Ethiopia

Ethiopia’s electric grid relies mostly on hydropower for electricity generation. Compared to metropolitan regions, rural areas have only 5% access to power, and 83% of remote areas rely on traditional biomass energy for lighting and cooking. Close to 60% of the land area in Ethiopia is pastoral, and electrifying from the main grid is a major challenge due to economic, technical, and nomadic reasons. This paper explores the feasibility analysis, design, and simulation of an off-grid solar Photovoltaic system in addition to discussing the complete engagement of national energy policy and a strategic plan for electrifying a pastoral Communities’ in Borena. Using solar radiation base data from the National Aeronautics and Space Administration, a case study is performed at Moyale, Yabelo, and Dire, which have 454 households, 367 households, and 379 households, respectively. Through the HOMER software simulation, the optimization determines the inverter, battery size and number, and solar array’s capacity. The daily energy usage and peak power demand in the pastoral communities of Moyale, Yabelo, and Dire were 498.102 kWh/day, 447.114 kWh/day, and 454.02 kWh/day, or 36.89 kW, 35.41 kW, and 35.68 kW, respectively. Due to the country’s subsidizing of all clean energy costs, off-grid solar Photovoltaic systems are more economically feasible than diesel generators, which have a level cost of electricity of 0.4 US$/kWh. National energy strategic plans and policies ultimately support the full involvement of off-grid solar Photovoltaic electrification at remote sites.

Day-Ahead Peak Load Probability Density Forecasting Based on QRLSTM-DF Considering Exogenous Factors

High-precision peak load forecasting is the guarantee of dispatching and decision-making in power systems. It is difficult to predict the peak power demand accurately and robustly due to many unstable factors that affect power loads. In order to solve this problem, this paper proposes quantile regression long-short term memory based on decoupling features (QRLSTM-DF) for probability density forecasting of the day-ahead peak load. This method reduces the possibility of mutual influence among characteristics by decoupling factors in different branches of neural network. Then, kernel density estimation (KDE) is used as a post-processing technique to generate probability density curves, which can give the comprehensive probability distribution of future peak loads and effectively quantify the uncertainty. Experimental results on three datasets show that the model outperforms several existing prediction models. In particular, the prediction results of the maximum daily peak power load and the peak load beyond the sample maximum boundary are also accurate and robust.

Predictive control of power demand peak regulation based on deep reinforcement learning

As urbanization continues to accelerate, effectively managing peak electricity demand becomes increasingly critical to avoid power outages and system overloads that can negatively impact both buildings and power systems. To tackle this challenge, we propose a novel model-free predictive control method called “Dynamic Dual Predictive Control-Deep Deterministic Policy Gradient (D2PC-DDPG)" based on a deep reinforcement learning framework. Our method employs the Deep Forest-Deep Q-Network (DF-DQN) model to predict electricity demand across multiple buildings, and based on the output of the DF-DQN model, applies the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize coordinated control of energy storage systems, including hot and chilled water storage tanks in multiple buildings. Experimental results show that our proposed DF-DQN model outperforms other traditional machine learning, deep learning, and reinforcement learning methods in terms of prediction accuracy, such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, our D2PC-DDPG method achieves superior control performance and peak load reduction compared to other reinforcement learning methods and an RBC-based control method. Specifically, our method successfully reduced peak load by 27.1% and 21.4% over a two-week period in the same regions. To demonstrate the generalizability of our D2PC-DDPG method, we tested it in five different regions and compared its performance with an RBC-based control method. The results showed that our method achieved an average reduction of 16.6%, 7%, 9.2%, and 11% for ramping, 1-load_factor, average_daily_peak, and peak_demand, respectively. These findings demonstrate the effectiveness and practicality of our proposed method in addressing critical energy management issues in various urban environments.

The Consumer and the Power Grid: Evolution of Problems and Solutions

Mass electrification during the 20th century translates most of the household energy needs into the electrical domain. Electrical energy topics such as distribution, metering, and quality are mainly considered successfully resolved. However, the Power Demand Peak problem remains open in real practice. An outline of such a problem is presented along with the proposal for decentralization of load handling to the level of a Distributive Transformer. A simulation model based on the European Commission Energy Efficiency Status Report statistical data has shown considerable potential for lowering daily peak energy consumption. The analyzed approach uses a widely available framework and simplifies the Power Demand Peak problem through handling a home appliance which activation can be postponed without expressively affecting consumer comfort. Such a decentralized approach enables the connection between the traditional power grid and the local Smart Grid without significant additional investments, relying on the already available information infrastructure.