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Extreme wintertime cold in the central United States (US) can drive excessive electricity demand and grid failures, with substantial socioeconomic effects. Predicting cold-induced demand surges is relatively understudied, especially on the subseasonal-to-seasonal (S2S) timescale of 2 weeks to 2 months. North American winter weather regimes are atmospheric tools that are based on persistent atmospheric circulation patterns, and have been linked to potential S2S predictability of extreme cold in the central US. We study the relationship between winter weather regimes and daily peak load across 13 balancing authorities in the Southwest Power Pool. Anomalous ridging across Alaska, the West Coast, and Greenland drive increases in demand and extreme demand risk. Conversely, anomalous troughing across the Arctic and North Pacific reduces extreme demand risk. Thus, weather regimes may not only be an important long-lead predictor for North American electricity load, but potentially a useful tool for end users and stakeholders. [ABSTRACT FROM AUTHOR]

The concept of time-of-use (TOU) electricity pricing is widely recognized as a key strategy to bridge the gap between electricity availability and consumption, enhance the efficiency of electricity, and refine the patterns of electricity usage. Nonetheless, the existing policy on pricing electricity based on TOU electricity pricing is missing a theoretical approach that evaluates the load properties and the advantages of investing in the power grid. Consequently, the article suggests a method for optimizing electricity prices based on TOU electricity pricing to reduce the costs associated with investing in power grids. Initially, a model for optimizing electricity prices based on TOU electricity pricing is developed, offering support for the pricing strategy of the power grid; Subsequently, a method for dividing TOU electricity pricing using the Gaussian Mixture Module (GMM) clustering algorithm is introduced, offering theoretical backing for the creation of such pricing strategies; Following this, a detailed optimization approach for electricity pricing of electricity TOU electricity pricing is suggested, along with taking into account the benefits of grid investments and the power grid's load properties, the formulation of the electricity pricing strategy for TOU electricity pricing; Ultimately, this approach is corroborated by the Chongqing power system in China, aiming to minimize disparities in peak load valleys and enhance the advantages of grid investments, thereby offering technical assistance for the scientific determination of TOU electricity pricing. [ABSTRACT FROM AUTHOR]

Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2. The comparison extends to popular optimization methods, such as particle swarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods. [ABSTRACT FROM AUTHOR]

In the Energy Internet, users no longer have a single demand response for electricity, but an integrated demand response for multiple types of energy. In integrated demand response, users participate in demand response directly by switching energy sources without having to change their energy usage time habits. A mathematical model of the electricity-heat-gas energy circuit, based on the correlation between different energy sources, considering the combined demand response economy and comfort of the household user. Real-time dispatch optimization modeling with the objective of real-time peak shaving within a day and tariff regulation as a means to provide a decision basis for real-time peak shaving and smoothing of the electricity load curve. An example is given to verify the effectiveness of the model and the feasibility of the solution method, at the peak of electricity consumption around 8 p.m., through real-time dispatching, the peak electricity load decreases by about 2 kWh, and the gas consumption increases slightly. And the economic basis of integrated demand response scheme decision-making is analyzed. [ABSTRACT FROM AUTHOR]

Electricity peak load forecasting plays an important role in electricity generation capacity planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple machine learning models have been implemented to forecast the monthly peak load in Thailand over the past few years, yielding promising results. One approach to further improve forecast accuracy is to effectively select the most accurate forecast value for each period from among the forecast values generated by these models. This article presents a novel reinforcement learning approach using the double deep Q-network (Double DQN), which acts as a model selector from a pool of available models. The monthly electricity peak load data of Thailand from 2004 to 2017 are used to demonstrate the effectiveness of the proposed method. A hyperparameter tuning methodology using a fractional factorial design is implemented to significantly reduce the number of required experimental runs. The results indicate that the proposed selection model using Double DQN outperforms all tested individual machine learning models in terms of mean square error. [ABSTRACT FROM AUTHOR]

As an energy source, solar cells can be central to peak load control in different power companies. Hydrogen production can be cost-effective if the generated electricity is unusable. Shades reduce solar cell current and fundamentally alter panel function, making it necessary to restore panels to optimum power generation. Owing to the issues outlined in this paper, the energy-economic-environmental effect of a shade detection algorithm was examined for different shade scenarios, and domestic-scale solar hydrogen production was evaluated. As the effect of the partial shade detection algorithm on the amount of solar hydrogen production has not been investigated quantitatively so far, this issue was discussed in this study. The algorithm and laboratory equipment developed by the authors were used to detect the partial shadow. Thanks to the respected reviewer, the following sentence was added to the abstract. The analyses in HOMER V2.81 software used 20-year average data for a residential building in the city of Tehran obtained from the NASA website. The results recommended that the designed algorithm increases hydrogen production in different scenarios between 5.8% and 32.9%. The results also revealed that the designed algorithm significantly lessen the pollutants between 100 kg/year and 240 kg/year. [ABSTRACT FROM AUTHOR]

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns from other normal weekdays and weekends. The performance of the hybrid model is tested with real electricity peak load data provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Experimental results show that the hybrid model gives the best performance while saving computation time by solving the problems in input variable selection, data decomposition, and imbalance data of normal and special days in the training process. [ABSTRACT FROM AUTHOR]

• An optimization framework is introduced to determine the new electricity rate, considering demand-side power capacity limitations. • An EV connection matrix was developed to make charging/discharging scheduling algorithm implementation much easier. • The modified particle swarm optimization (PSO) algorithm increases the convergence rate of the algorithm. • Simulation results revealed that the TOU rates should be applied according to a specific pattern, depending on the location. This paper introduces a framework to yield an electricity rate for vehicle-to-grid (V2G) charging station (CS) to minimize installation capacity of a charging station considering electric vehicle (EV) arrival/departure time distribution. Two different layers are designed to avoid an obstacle encountered when formulating the problem as a convex optimization and to represent an EV aggregator and an electricity rate decision maker – a regulator. The EV aggregator layer focuses on increasing the profit and the regulator minimizes the peak load of the V2G CS. Linear programming was formulated for the former layer, and a modified particle swarm optimization (PSO) method was developed for the latter. Modification of the PSO approach allowed for easier escape of local minima, resulting in a new electricity rate for the V2G CS based on the EV arrival/departure time distribution data. The algorithm employs new matrices devised in this paper to accommodate EV information in the optimization process. In a simulation study, two distinct CSs with V2G operations were evaluated, each with a different EV arrival/departure time distribution. The simulation revealed that the peak load and the profit of the aggregator vary dramatically depending on the arrival/departure time distributions. [ABSTRACT FROM AUTHOR]

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. [ABSTRACT FROM AUTHOR]

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %. • Establish an average load peak valley difference index. • Establish a RIES planning framework includes network layout and capacity planning. • Establish an IES planning model with the goal of cost and carbon emissions. • Total annual cost decreased by 7.13 % and CO 2 emissions decreased by 2.77 %. [ABSTRACT FROM AUTHOR]

With the wide spread of new variants of coronavirus that cause the infectious disease COVID-19, governments around the world typically respond by imposing restrictions on people's activities that range from partial to full lockdowns. This has severe implications on all economic activities, which is manifested by the changes in energy demand. In this study, the impact of COVID-19 on the electricity sector in Jordan is analysed through quantifying the strictness of the government response measures to contain the spread of the pandemic, as calculated by the stringency index, with the electricity demand by the different sectors. Results showed that the minimum peak load in 2020 decreased by 13% as compared to that of 2019. The most affected sectors were the domestic sector, whose share in consumption increased by 8%, and the commercial and hotel sector, whose share decreased by 19%. The concept of an energy-weighted stringency index was introduced to account for the impact of government response measures on the different sectors. The analysis was applied for all Jordan as well as for the three electricity distribution regions. Results also showed that despite measures taken to contain spread of the pandemic, the share of electricity generation by renewables increased from 15% in 2019 to 24% in 2020. [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [ABSTRACT FROM AUTHOR]

Typical approaches to assessing excess generation capacity in African electricity systems have considered the nameplate capacities of existing generation plants, the planned generation capacity, and peak load. However, the omission of variables such as the condition of transmission and distribution infrastructure, the age of existing power plants, and whether generation capacity is intermittent (as in the case of wind or solar) or subject to climate impacts (as in the case of hydropower) has caused such assessments to be misleading. This can result in misguided public policy and power procurement processes that are poorly matched to the needs of evolving power systems. In this paper, we analyze the case of Kenya to show how power system planning must consider these key factors that are often ignored. Far from having excess capacity as has been repeatedly reported, the Kenyan grid has limited capacity to take up additional load at present. [ABSTRACT FROM AUTHOR]

Endowed with over 40,000 MW of economic hydropower potential relative to its nascent domestic demand, Nepal can export electricity to its power-deficit neighbours in the BBIN (Bhutan, Bangladesh, India and Nepal) region as a strategic economic opportunity. The untapped hydro potential in Nepal, coupled with BBIN's complementary seasonal demand patterns, difference in peak load timing, and rapidly growing growth of electricity demand in Bangladesh and India, are favourable for regional electricity cooperation, especially for Nepal. This study uses the SWOT-AHP-TOWS approach to identify the key barriers and opportunities for cross-border electricity trade and to provide insights on possible strategies for Nepal. [ABSTRACT FROM AUTHOR]

One of the main challenges in the world as well as in Latvia is the reduction of the fossil fuel consumption. Electricity generation, which is still mainly produced by fossil fuel or nuclear power, is one of the largest shares. Nowadays end-users can efficiently control their electricity consumption by shifting electrical loads and taking into account fluctuating electricity price at Nord Pool. The smart metering systems have been expanding, but for the time being, end-users do not use this possibility efficiently. This study provides an analysis of Latvian electricity consumption specifics and evaluates a potential to shift electricity peak loads. The study includes households, industry and commercial/public sector. Based on the questionnaire distributed among end-users, the initial data on equipment and usage profile were collected. The most essential equipment was reviewed to estimate the potential for load shift. The obtained results show that that there is a positive effect of the load shift. In the household sector, the average electrical load shift can be 6 % (95 GWh/ year) during a period at a cheaper price; in the industry sector on average 37 % (36.58 GWh/ year) and in the commercial/public sector there is no potential due to its specifics (working hours, constant use of refrigerators, etc.). [ABSTRACT FROM AUTHOR]

• 20 years of electricity load for the Brazilian grid have been evaluated on hourly basis. • Scenarios for the electricity supply in 2030 and its impact in hydropower management were investigated. • The results indicate an increased peak hydropower demand, that endure longer. • A massive investment in solar water heaters could contribute significantly to the reliability of the national grid. The demand for electricity and the need to replace fossil fuels with renewables have been growing steadily, and this transition will have significant implications for our world that are only beginning to be understood. Brazil is one important example of a big economy where the electricity is already supplied by renewables, such as hydro, wind, and biomass-fired thermal power. In this work, we investigated the electricity load curves in the last 20 years in Brazil, and four different scenarios for 2030 are proposed to evaluate the impact of increasing renewables in the national grid, on an hourly basis. The analysis shows that growing electricity demand and the expected reduction in the hydropower share will significantly affect the reliability of the national grid, due to higher peak load and also due to the intermittency of Solar and Wind. Without any gigawatt-scale hydropower projected for the near future, increasing the share of these renewables should push hydropower to operate hundreds of hours every year above typical peak power levels experienced in the past. To avoid or reduce the threat related to this trend one of our scenarios suggests that solar water heaters could be massively deployed in Brazil, which would positively impact the system reliability by reducing the electricity demand mostly at peak loads during early evenings. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to estimate lost consumer values due to various restrictions on household electricity use involving behavior adaptation. To do this, we conduct a choice experiment where households choose between hypothetical electricity contracts including various restrictions on the use of high-power household appliances. In addition, we use a contingent valuation question related to complete blackouts to study a restriction on other types of electricity usage (heating, lighting, TV, etc.). The results indicate a significant difference between the value lost due to the soft control, and the blackouts. Furthermore, policies aiming at stimulating behavioral changes are costly and it is far from obvious that demand response requiring behavioral adaptation is more cost effective than supply response (i.e., increased production of electricity). [ABSTRACT FROM AUTHOR]

With the implementation of clean energy heating project in rural areas of northern China, the pollution of coal-fired heating in winter has been greatly improved. However, the use of large-scale electric heating equipment put forward new challenges to the power supply reliability of low-voltage distribution network. Especially in rural power grid, the load is heavy in winter and low in other seasons. It is one of the effective measures to achieve optimal resource allocation and improve the power supply quality of the distribution grid to exploit the demand response potentialities of electric heating loads, and motivate electric heating loads to actively respond to demand response programs (DRP) for peak-load shifting. Therefore, a load optimal control strategy for rural electric heating equipments based on demand side peak load regulation was proposed in this paper. A market transaction model was designed for third-party agency companies to represent coal-to-electricity users in the peak shaving market. The agency company made the centrally control strategies of electric heating users’ equipment according to the peak shaving volume and corresponding price obtained from the peak shaving market bidding. The electric heating users were classified based on the controllability of each user by the agency company. Each kinds of users could get different compensation price to encourage them to participate in the demand response programs for peak-load shifting. In this market model, a multi-objective optimization model was established to control the users’ air source heat pump. The goals of the optimization model were to meet the user's temperature demands to the maximum extent and to maximize the benefit of the agent company. Meanwhile, user's comfort requirements for indoor temperature were considered in this model, and the user classification compensation mechanism was introduced to improve the user's initiative to participate in peak load regulation. Taking the load data of electric heating users in Pinggu District of Beijing as an example, the simulation analysis was carried out. The results show that the total load reduction of all electric heating equipment was 159.773 kWh, there was no over-control for anyone user, the load reduction of each user fluctuated around average reduction of 7.989 kWh, the peak valley of the user's power consumption curve was changed, the optimal control strategy proposed in this paper played the role of peak load reduction and valley filling, which can relieve the pressure of the upper power grid during peak load period. The indoor temperature of the users was (18±1) ℃, which meet the demand of comfortable temperature for heating in winter. Compared with the target of not considering the benefit, the one-day compensation cost of the agency company was saved 31.9%, and the users with the highest degree of control got the most compensation income, the peak load regulation strategy could encourage the users to participate in the peak load regulation actively. [ABSTRACT FROM AUTHOR]

Abstract Indonesia's electricity demand is growing rapidly, driven by robust economic growth combined with unprecedented urbanization and industrialization. Energy-efficiency improvements could reduce the country's electricity demand, thus providing monetary savings, greenhouse gas and other pollutant reductions, and improved energy security. Perhaps most importantly, using energy efficiency to lower peak electricity demand could reduce the risk of economically damaging power shortages while freeing up funds that would otherwise be used for power plant construction. We use a novel bottom-up modeling approach to analyze the potential of energy efficiency to reduce Indonesia's electricity demand: the LOAD curve Model (LOADM) combines total national electricity demand for each end use—as modeled by the Bottom-Up Energy Analysis System (BUENAS)—with hourly end-use demand profiles. We find that Indonesia's peak demand may triple between 2010 and 2030 in a business-as-usual case, to 77.3 GW, primarily driven by air conditioning and with important contributions from lighting and refrigerators. However, we also show that appliance and lighting efficiency improvements could hold the peak demand increase to a factor of two, which would avoid 26.5 GW of peak demand in 2030. These results suggest that well-understood programs, such as minimum efficiency performance standards, could save Indonesia tens of billions of dollars in capital costs over the next decade and a half. Highlights • Electricity demand in Indonesia is projected to triple in the next 20 years. • A few key consumer appliances are found to drive evening peak demand. • Achievable energy efficiency goals could reduce peak load by 26.5 GW. • Energy efficiency programs could save billions in capital investment in developing countries. [ABSTRACT FROM AUTHOR]

Highlights • A DR of power credit model was established. • Virtual RTP was introduced based on power credit scheme. • Considering consumer comfort while optimizing cost. • Cost & uncertainty analysis was conducted to prove rationality of the model. Abstract In view of real-time pricing implementation difficulties and certain deficiencies of the time-of-use price in China, a novel virtual real-time electricity pricing scheme is proposed based on power credit incentive program in this paper, which can help to achieve better results for peak load shifting than the conventional time-of-use price-based scheme. In addition, it can also work as virtual real-time pricing mechanism. The aim of this study from the perspective of load scheduling of residential appliance device, focuses on the optimization of cost and willingness to change of the user, and further examining the feasibility and effectiveness of electricity pricing mechanism in detail. Finally, the application prospect of the electricity pricing mechanism in demand response is evaluated and the performance of the model in our study established with illustration from our simulation results. [ABSTRACT FROM AUTHOR]

• A MILP model is introduced to regulate peak demand. • Efficient coordination of DERs and load shifting to improve LF. • Optimal bulk scheduling of equipment used in the EN. • Mutual benefits for customers and the energy company. • The proposed model found the optimal solution to the problem. In recent decades, in urban centers, several services (such as health, food, security, connectivity, etc.) have become more dependent on electricity. With the population increase, the energy requirements of these services can lead the electricity network (EN) to a state of operational stress. In this scenario, distribution companies (DISCOs) need to develop efficient energy management strategies to avoid wasting electricity in its EN. The load factor (LF) of an EN is an indicator related to efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers, together with the operation of distributed energy resources (DERs), such as renewable energy sources (RESs) and capacitor banks (CBs), represents a promising alternative. In order to address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DERs in the EN. Operational constraints related to the performance of EN, RESs, and CBs are considered. Uncertainties in the habitual demand profiles and solar irradiance during the day are represented through simulation algorithms. In order to evaluate the proposed model, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring an efficient EN operation. [ABSTRACT FROM AUTHOR]