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This study proposes an optimized day-ahead economic dispatch framework for wind-integrated microgrids, combining energy storage systems with a hybrid demand response (DR) strategy to address real-time grid pricing dynamics. The model evaluates five operational scenarios: (1) conventional dispatch without renewable/storage/DR integration, (2) wind power participation, (3) coordinated wind-storage operation, (4) wind-DR synergy, and (5) full integration of wind, storage, and DR. A two-stage demand response mechanism is developed, integrating incentive-based load adjustments with price elasticity modeling through a tariff scaling factor approach. The analysis compares operational costs, renewable energy utilization efficiency, load profile characteristics, and user comfort levels across all scenarios. Results demonstrate that the combined deployment of wind generation, battery storage, and adaptive DR significantly reduces microgrid operating costs while enhancing peak load management. The integrated strategy proves most effective in balancing supply-demand dynamics, improving grid stability through synergistic storage-DR coordination, and maintaining user satisfaction. Case studies validate the framework’s practicality in achieving cost-efficient dispatch decisions without compromising renewable energy integration capabilities. The proposed model achieves a 23.4% reduction in operational cost and over 88% utilization of renewable energy, with load peaks significantly flattened and user comfort exceeding 90% throughout the scheduling horizon.

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

The Estonian retail electricity market opened on the 1st of January 2013. The wholesale electricity market has been operating open successfully for some time already. The liberalized electricity market creates new opportunities for consumers. From 1 January 2013, all electricity producers compete on power exchange. This means that the price of the electricity produced from oil shale is no longer regulated by the State and Narva Power Plants, like other power companies, are competing in the open electricity market. The liberalized electricity market and new remotely readable meters enable retail dealers to offer consumers more flexible packages. For example, a new pricing system could be developed that takes into account actual costs of electricity production at the exact time these are made. The aim of this article is to give an overview of possibilities of real-time pricing and compare the existing tariff systems to the hourly variable pricing tariff system.

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

Rising costs for energy are increasingly becoming a vital factor for the production planning of manufacturing companies. Manufacturers face the challenge to react to dynamic energy prices and design energy cost efficient schedules in their production planning. In the literature, the energy cost-aware Flexible Job Shop Scheduling Problem addresses minimization of both makespan and energy costs. Recent studies provide multi-objective approaches to model the trade-off of minimizing makespan and energy costs. However, the literature is limited to coarse-grained time periods and does not consider dynamic tariffs where costs change at short intervals, so that production schedules may fall short on energy costs. We aim to close this research gap by considering frequently changing real-time energy tariffs. We propose a multi-objective memetic algorithm based on the non-dominated sorting genetic algorithm (NSGA-II) with both makespan and energy cost minimization as the objectives. We evaluate our approach by conducting computational experiments using prominent FJSP-benchmark instances from the literature, which we supplement with empiric dynamic energy prices. We show results on method performance and compare the memetic NSGA-II with the results of an exact state-of-the-art solver. To investigate the trade-off between a short makespan and low energy costs, we present solutions on the approximated Pareto front and discuss our results.

The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

Electricity theft is a notable aspect of power distribution utilities due to advance in the non-technical loss. It results imbalance between power supply and demand. It consequence overload of the distribution network and extraneous tariff invoke on legally connected consumers. The advance metering infrastructure is useful for an energy audit of every distribution transformer due to a communication facility. However, direct hooking on distribution overhead line or tapping from underground cables remains an interminable issue which has to be rigorously decimated. The objective of this study is to present real-time electricity theft detection and prevention scheme (ETDPS) with the available infrastructure in the field. The proposed ETDPS is based on programmable logic control; it identifies the pilferage locations and estimates the power stolen by illegal consumers. The prototype is tested in the laboratory and the results demonstrate that the ETDPS works satisfactorily under diversified operating conditions. The proposed scheme is implemented as a part of their Smart City Pilot Project by Maharashtra State Electricity Distribution Company Limited, Nagpur (India) and the performance demonstrates its feasibility.

Electricity prices can be an important driver of inflation. However, the transmission from wholesale electricity price to inflation depends on the type of pricing mechanism in the retail market. Using the introduction of Real-Time Pricing (RTP) in the Spanish retail electricity market, we analyze the transmission of wholesale electricity price shocks to inflation dynamics and how this transmission is affected by the introduction of RTP. The main findings are that—after RTP was rolled out—electricity price shocks display a swifter transmission to inflation while the maximum impact is around three times higher than in the previous period. This higher transmission is not only generated by a more pronounced reaction of the retail electricity price but is also caused by a greater transmission of these shocks to core inflation. These results imply that introducing RTP impacts inflation dynamics and, therefore, affects how central banks should react to this type of shock. JEL Classification: E31, Q43, Q48