Peak Load Hours Research Articles

Spike load smoothing strategy considering behavioral decision under vehicle-road-network convergence

To reduce the impact of large-scale electric vehicles on traffic and electric load peaks, we propose a "vehicle-road-network" integration strategy that takes into account behavioral decisions. To reduce the impact of large-scale electric vehicles on traffic and electric load, we propose a peak load suppression strategy that takes into account behavioral decisions under "vehicle-road-network" integration. Firstly, a "vehicle-road-network" fusion model is constructed based on the large-scale access of EVs; then, a load model is built considering the large-scale access of EVs; finally, based on the third-generation foreground theory, the behavioral decision of EV users is analyzed based on the operation state of the fusion system, and a decision model is built by the foreground value to minimize the operation cost. The spike load smoothing strategy scheme is established with minimum operation cost. By using the fusion of Sioux Falls urban road network system and IEEE33 node grid system as the simulation example, the simulation results show that the proposed peak load smoothing strategy can coordinate the distribution of charging load and relieve traffic pressure during peak load hours, and reduce the travel and charging costs of EV users.

Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition

Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.

Cost-effective sizing method of Vehicle-to-Building chargers and energy storage systems during the planning stage of smart micro-grid

Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.

Assessing Voltage and Frequency Instability in Renah Kemumu's Micro Hydro Power Plant

The increasing demand for energy and the need for environmental sustainability have led to the adoption of renewable energy sources, such as micro-hydropower plants (PLTMH), in isolated communities in Indonesia. However, voltage and frequency instability issues are commonly encountered in PLTMH systems, which undermine their performance. In 2009, a micro-hydropower plant with a capacity of 30 KW was built in Renah Kemumu village. Ranah Kemumu village is located in Jangkat sub-district, Merangin district, Jambi province. In this study, the voltage and frequency instability of PLTMH in Renah Kemumu Village were investigated. The methodology employed in this study was observational. The voltage and frequency data were measured and recorded during peak load hours from 18.00 to 21.00 over one month. The percentage increase relative to the standard voltage and frequency was calculated. Boxplots were generated to visualize the distributions. The voltage measurements showed maximum and minimum values of 270 V and 264 V, representing increases of 22.72 % and 20.45% from the 220 V (standard set by the power utility (PLN)). The frequency ranged from 66.32 Hz to 57.46 Hz, corresponding to 30.92% and 14.92% deviations from the 50 Hz (standard set by the power utility (PLN)). These values exceeded the tolerance limits of the State Electricity Company of Indonesia. The analysis indicates that the voltage and frequency levels at the PLTMH in Renah Kemumu Village are unstable during peak loads, with fluctuations stemming from changing water flows, load currents, and weather conditions

Mitigation of Renewable Energy Source Production Variability in Residential Stock through Small-Scale Gas–Liquid Energy Storage Technology Application

Despite renewable energy source integration being a well-established requirement in international policies, energy systems still face some unresolved issues, including the intermittence of production. To tackle this problem, a viable solution could comprise the off-peak storage of electricity production excess, to be consumed later during peak-load hours. The transition from the diffuse pattern of centralized generation to the distributed model, involving energy communities, suggests an additional aspect to manage: the spatial constraints of systems for domestic applications. Compressed-air energy storage represents a promising Power-to-Power technology for small-scale energy integration. This study proposes the application of a gas–liquid energy storage system (GLES) in a residential building, using renewable energy excess from a photovoltaic (PV) array. The performance of the proposed system, whose operation involves the compression of the gaseous mass through a piston operated by mineral oil, was evaluated through energy analysis performed simulating the devices and their coupling with the load profiles of the building. The thermodynamic model of storage was validated using data from an experimental campaign on a prototype. A sensitivity study, acting on the features of the system, such as the compression rate and vessel size, allowed us to compare the absorbed PV energy excess, the coverage of the building energy demand during the expansion phase, and the electrical efficiency of a daily cycle. The results obtained, together with the related economic analysis, were used to quantify the market potential of the proposed solution, to be exploited as a mechanical alternative to conventional electric batteries in dwellings.

Droop parameter-based economic dispatch for islanded microgrid considering demand response

The droop parameter setting of the distributed generator (DG) has an essential role in islanded microgrid (MG) systems. This paper presents droop-based economic load dispatch (ELD) in an islanded MG. In this approach, the Power–frequency (P–f) droop characteristic of DG is integrated into its incremental cost, which is the derivative of the cost function of DG. The optimisation problem is formulated by considering the standard deviations of the incremental costs of DGs. Under steady-state conditions, DGs operate at a single frequency and the P–f droop-based ELD ensures that the incremental costs of DGs are equal, minimising the overall cost of active power generation, following the equal incremental cost principle. Furthermore, an incentive-based demand response (DR) programme is integrated into the system to maximise the utility benefit function by incentivising customers for power curtailment. The results exhibit significant power curtailment during peak load hours, indicating the effectiveness of the DR programme. The proposed combined mathematical model is applied to the modified 33-bus islanded MG and solved using basic particle swarm optimisation. It calculates optimal droop parameters for a day-ahead scenario and emphasises the efficiency benefits of optimised droop settings and the integration of DR in islanded MGs.

Iran’s Participatory Power Market Regarding Distributed Generation from Renewable Sources: A Case Study

To study initial conditions for participatory power market formation based on renewable energy sources (RES) in Iran after complete accomplishment of the economic evolutionary plan, a case study for a residential building has been conducted to consider the effects of the most important parameters. So, a model of power market including three parts: consumption, production and regulations, has been optimized to achieve to a micro cost effective system of participatory power market regarding distributed generation from RES. The results indicate that the power tariff structure and rates, RES technology capital cost and economic conditions are prominent parameters. As well, to initiate the power market, a revised power tariff structure and rates are suggested; namely, a plan for shifting peak load hours from evening to day hours is essential. Also, it is demonstrated that the policy of establishing a participatory power market included photovoltaic distributed generation systems must be pursued in the economic evolutionary plan with emphasis on development of zero energy cost buildings.

A convex multi-objective distributionally robust optimization for embedded electricity and natural gas distribution networks under smart electric vehicle fleets

The indisputable environmental concerns have forced the imminent proliferation of renewable energy sources (RES) and electric vehicles (EV). However, the high penetration of such uncertain and variable sources, can pose significant challenges for maintaining supply–demand balance in electrical distribution networks (EDNs). To address these challenges, this paper presents a distributionally robust optimization (DRO) method for multi-objective scheduling in integrated electricity and natural gas distribution networks (IENGDNs). The proposed approach aims to minimize environmental-economic objectives while taking into account the high penetration of EVs and RESs. The impact of a smart EV charging strategy is evaluated to reduce operating costs and maximize the use of RESs. Additionally, demand response programs (DRPs) are used in the EDN to prevent overlapping of peak load hours between the EDN and natural gas distribution network (NGDN). Linepack technology is also used to store natural gas in NGDN pipelines, which increases the short-term flexibility of the entire IENGDNs. The proposed problem is mathematically structured as a second-order conical programming (SOCP) model to benefit from the reliable and efficient convex optimization solution. The simulations were conducted on a 123-EDN and a 40-NGDN systems. Different simulation cases show that the proposed economic-environmental framework can bring down the total emissions by 10.02%.

Research on fair residential critical peak price: Based on a price penalty mechanism for high-electricity consumers

We construct a fair residential critical-peak price (CPP) optimization model based on a penalty mechanism to address the peaking pressure on the grid and the cost of new energy abandonment caused by the yearly increase in residential electricity load and the significant increase in new energy capacity. According to the electricity consumption habits of different residential consumers, we use the K-means clustering method to identify and classify high-electricity consumers (HECs), low-electricity consumers (LECs), and normal-electricity consumers (NECs) during the critical peak and valley load hours respectively, and design a penalty mechanism of electricity price for HECs (PMEP-H) that penalizes HECs during the critical peak load hours, and compensates the total penalty amount to LECs during the critical peak load hours and HECs during the valley load hours, then calculate the residential electricity load after implementation of PMEP-H based on consumer psychology. The CPP optimization model is constructed by considering the constraints of consumer satisfaction with electricity, tariff penalty, compensation balance, penalty and compensation price, power balance and new energy-generating units output power, and the objective function of high comprehensive system efficiency represented by the difference between peak cost of thermal generation, the benefit of new energy consumption and benefit of the grid side. The model is then solved using an improved particle swarm optimization algorithm for a real residential area in a populated area in northern China. The results show that the developed model can connect the generation, grid and user sides through the electricity consumption tariff, which generates considerable benefits in both peak cutting and new energy consumption targets and can enhance the comprehensive benefits of the generation, grid and user sides system to achieve the supply-grid-load linkage, providing a reference for the improvement of the CPP mechanism in China.

Local demand management of charging stations using vehicle-to-vehicle service: A welfare maximization-based soft actor-critic model

Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

Maximum Tolerated Number of Simultaneous BEV Charging Events in a Typical Low-Voltage Grid for Urban Residential Area

In this paper, we determine the maximum number of battery electric vehicles (BEVs) that can be charged simultaneously at full power during peak load hour without overloading transformer and lines or causing an unacceptable voltage drop in the low-voltage (LV) grid. In order to predict the BEVs charging demand, an application that takes into account the random user’s arrival time and the initial battery state of charge (SOC) was developed using the C++ programming language and the Qt toolkit. The network analysis was then carried out using the Quasi-Dynamic Simulation (QDS) toolbox in DIgSILENT PowerFactory on a typical German LV grid for a metropolitan urban area. The simulation findings indicate that the value of simultaneity factor (SF) plays an important role in identifying the most robust and weakest grid’s bottlenecks. There is currently no immediate threat of electromobility pushing the parameters of the grid to their unacceptable limits; however, it is essential to examine the LV grid’s bottlenecks and gradually prepare them for the ramp-up of BEVs. In the short term, the bottlenecks can be removed using conservative planning and operating principles; however, employing novel approaches will be crucial in the longer term.

Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load

As the number of electric vehicles (EVs) connected to the grid increases, the EV electricity demand rises dramatically, affecting the grid’s planning and operation and deepening the coupling of the power and transportation systems. Therefore, accurate spatial–temporal distribution prediction of EV charging load is vital for both power system and coupled power-transportation system studies. This paper proposes a novel method based on stochastic user equilibrium (SUE) for predicting the accurate spatial–temporal distribution of EV charging load synchronized with traffic states. A prediction framework of EV charging load based on SUE and trip chain is proposed, which can effectively reflect the actual behavior of EVs in synchronous traffic states. Then, the extended logit-based SUE and equivalent mathematical model are proposed to obtain more detailed traffic states with intersection turning flows and delays. Meanwhile, the unified reachability and charging models are established to ensure that the trip chain is reachable and the charging characteristics are suitable for different EV types. Finally, the method of the successive averages (MSA) and the Dijkstra-based K-shortest paths algorithms are integrated to solve the proposed framework iteratively with stable convergence. Test results on a realistic traffic network show that the proposed method can effectively reflect the charging and trip characteristics of different EV types while ensuring reachability. And it can also accurately predict the overall and individual EV travel costs and total charging loads in detailed synchronous traffic states. In particular, even in the case of high EV penetration with higher peak-to-valley differences and charging demand, the convergence of the prediction is still stable with even more remarkable prediction effectiveness, especially during peak load hours. Furthermore, the quantitative analysis based on proposed criticality indexes reveals that traffic network failures will affect the network-wide traffic states and EV charging loads with different node-level impact characteristics, which should be considered in joint power-transportation restoration scheduling.

Predictive investigation of the combined heat storage system using at NPPs with VVER

The increase in the share of nuclear power plants in the energy systems leads to the need for their participation in covering the irregularities in the electrical load schedules. NPPs unloading is economically unsustainable. Combining NPPs with storage systems can be a solution to this problem, as it will allow the reactors to operate at a constant capacity utilization factor, while excess energy will be stored during load dip hours and converted into electrical energy during peak load hours. The authors have developed a thermal storage system based on the combination of a phase change accumulator with hot and cold water tanks. The system also contains a steam turbine to generate electricity during peak hours using stored energy. Earlier the authors proved that a low-power turbine can be used to supply power to the NPP auxiliary needs with a blackout using only residual heat energy. In this paper, a methodology for determining the necessary operating parameters of the storage system for using under the required conditions is presented. The investigation of economic efficiency was carried out on the example of a range of electricity tariffs for the world’s energy systems. Accumulated net present value ranged from $0 to $85 million.

A Bidirectional Onboard Charger With Multistep Constant Current Charging Capability

This article realizes a bidirectional onboard charger (OBC) for light electric vehicles (LEVs), with multistep-constant-current (MSCC) charging capability. In this MSCC charging operation, the battery current is reduced during the peak-load hours and resumes the rated charging operation amid the off-peak load hours. Since this MSCC operation is a wide load range operation, the controllers of the front-end converter (FEC) and back-end converter are modified. In this context, a Hermite polynomial-derived functional link neural network (H-FLNN)-based deadbeat predictive current controller (DPCC) is introduced to improve the performance of the FEC of the OBC. Moreover, the HFLNN-based DPCC for the FEC regulates the grid current quality during MSCC and distinct operating conditions such as grid voltage sag and swell, distorted grid voltage, and the presence of dc offset error in the measured grid voltage. A laboratory prototype of a 1.1-kW rating is developed to verify the efficacy of the H-FLNN DPCC to keep the grid current power quality (PQ) within the International Electrotechnical Commission (IEC)-61000-3-2 standard throughout the operation. Moreover, the effectiveness of the design is verified amid the grid voltage sag and swell and distinct operating conditions. The experimental analysis depicts that the rated efficiency of the developed laboratory prototype is 90.18% and the analytical power density is 64.16 W/in3. Furthermore, the vehicle to grid (V2G) operation validates the satisfactory design of OBC for bidirectional operation. At last, the comparison of the presented system with existing systems is given to summarize the advantages of the presented system over the existing systems.

A Decentralized Dynamic Pricing Model for Demand Management of Electric Vehicles

Transportation electrification is considered a green alternative to internal combustion vehicles. However, higher penetration of electric vehicles (EVs) can cause several technical challenges to the power systems, including local equipment overloading. This is especially challenging for distribution systems due to the direct connection of EVs with them. To mitigate equipment overloading, by managing EV loads locally, a dynamic pricing model is proposed in this study. First, a satisfaction function is devised for EVs considering the sensitivity of different EV owners to the battery state of charge and urgency of recharge. Then, a welfare function is developed for the EV fleet operator (EFO) considering the net load of EVs and the electricity price of the upstream grid. To this end, a welfare maximization problem is formulated considering the welfare of EVs and EFO. The problem is then decomposed into the EFO sub-problem and EV sub-problem to preserve privacy. Finally, a distributed mechanism is developed to solve the sub-problems iteratively without revealing private information. The performance of the proposed method is analyzed for a residential apartment complex in terms of load management and convergence for different day types (working days and holidays). Simulation results have shown the efficacy of the proposed method in mitigating the overloading of transformers during peak load hours.

Power Scheduling with Max User Comfort in Smart Home: Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently. A home energy management system (HEM) is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy. Here, we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm (GWA) and Harmony Search Algorithms (HSA). Moreover, a fusion initiated on HSA and GWA operators is used to optimize energy intake. Furthermore, many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge. Hybridization has proven beneficial in achieving numerous objectives simultaneously, decreasing the peak-to-average ratio and power prices. Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method, Harmony GWA (HGWA). The simulations are for a multi-family housing complex outfitted with various cool gadgets. The simulation results indicate that GWA functions better regarding cost savings than HSA. In reputes of PAR, HSA is significantly more effective than GWA. The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR, as opposed to 503.4 in GWA, 398.10 in HSA and 640.3 in HGWA. The suggested approach performed better than HSA and GWA in PAR reduction. For single-family homes in HGWA, GWA and HSA, the reduction in PAR is 45.79%, 21.92% and 20.54%, respectively. The hybrid approach, however, performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

A novel peak shaving approach to improving load flexibility of the Allam cycle by integrating cold energy storage

Allam cycle is a promising alternative cycle for power generation, characterized by high net electrical efficiency and near-zero emissions. For efficient and load flexible operation, a novel system integrating Allam cycle and cold energy storage system (CES-Allam) has been proposed. The compression train is employed to increase the pressure of CO2-rich working fluid for both Allam and refrigeration subsystems. The ice slurry is used as a cold energy storage carrier, which is further used to assist in liquefying carbon dioxide (CO2) at a lower evaporating pressure, and leads to higher electrical power output during peak load hours. The thermodynamic and economic models of this integrated system have been developed for comprehensive evaluation. The results show that the proposed integrated system is technically feasible for load flexible operation with a wider range of power generation regulation capacity (28.46%–105.97%) than that of the conventional Allam system (40%–100%). The round trip efficiency is 71.09% for the cold energy storage system, which is equivalent to the adiabatic compressed air energy storage system. The payback period is 4.15 years for the cold energy storage subsystem, which indicates that the CES-Allam system is economically feasible and competitive. Moreover, the sensitivity analyses of electricity prices and natural gas shows that the proposed CES-Allam system is more suitable for areas with higher natural gas prices and greater differences in peak and valley electricity prices. The present study is expected to provide a detailed reference and guidance for the use of cold energy storage to improve the load flexibility of the Allam system.

Variability in electricity consumption by category of consumer: The impact on electricity load profiles

Residential electrification of transport and heat is changing consumption and its characteristics significantly. Previous studies have demonstrated the impact of socio-techno-economic determinants on residential consumption. However, they fail to capture the distributional characteristics of such consumer groups, which impact network planning and flexibility assessment. Using actual residential electricity consumption profile data for 720,000 households in Denmark, we demonstrate that heat pumps are more likely to influence aggregated peak consumption than electric vehicles. At the same time, other socio-economic factors, such as occupancy, dwelling area and income, show little impact. Comparing the extrapolation of a comprehensive rollout of heat pumps or electric vehicles indicates that the most common consumer category deploying heat pumps has 14% more maximum consumption during peak load hours, 46% more average consumption and twice the higher median compared to households owning an electric vehicle. Electric vehicle show already flexibility with coincidence factors that ranges between 5 and 15% with a maximum of 17% whereas heat pumps are mostly baseload. The detailed and holistic outcomes of this study support flexibility assessment and grid planning in future studies but also the operation of flexible technologies.

Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems

The load requirement of a microgrid system fluctuates on an hourly manner. As the load demand rises and falls, utilities set various rates at different hours of the day, which is known as the time-of-usage (TOU) based electricity pricing. The two types of load that make up the microgrid system's hourly demand are elastic and inelastic. Demand-side management (DSM) restructures the entire demand model based on demand-price elasticity by moving elastic loads from peak load hours to those hours when the utility charges less. This research employs a hybrid intelligence method to reduce the total cost of three microgrid systems taking into account the implementation of DSM. Practical complexities like unit-commitment of the dispatchable fossil fuelled generators were considered. Numerical results project noticeable savings ranging from 8% to 18% for all the test systems when the DSM strategy was incorporated. A detailed comparative analysis with the results available in the literature corroborates the effectiveness of the proposed hybrid approach. Measures of central tendencies, non-parametric statistical analysis and algorithm execution time show better results of the hybrid optimization tool presented in this paper.

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.