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Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid's ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy's volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC's involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer efficient, environmentally friendly power at a reasonable price while addressing the needs of both power providers and customers. This study also looks into the effects of V2G mixing on voltage stability, LMP (locational marginal price), and the effectiveness of a TCSC in a deregulated electrical system. MiPower software is used to simulate operation and find the optimal placement of the storage unit within the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into smart distribution grids poses substantial challenges in maintaining grid stability, efficiency, and reliability due to their inherent variability and intermittency. This study addresses these challenges by proposing a novel two-level optimization model aimed at enhancing operational efficiency and robustness in smart distribution grids. The model synergistically integrates renewable energy sources, energy storage systems, electric vehicles, and demand-side management through a dynamic reconfiguration approach. It employs a robust optimization framework combined with a two-stage second-order cone optimization model to manage real-time operations and strategic grid reconfiguration. Key findings from simulations on the IEEE 33 and 69-bus networks underscore the model's effectiveness. In the 33-bus system, implementing the demand response program led to a significant reduction in power losses, from 0.64 MW to 0.52 MW, and improved voltage stability, with the minimum voltage increasing from 0.970 to 0.980 p.u. Similarly, in the 69-bus system, power losses decreased from 0.85 MW to 0.79 MW, and voltage stability improved, with the minimum voltage rising from 0.962 to 0.972 p.u. The model also demonstrated reduced energy procurement needs, showcasing its impact on enhancing grid efficiency and reliability. These results highlight the model's potential for advancing smart grid management strategies, offering significant improvements in operational performance and stability under varying demand conditions. [ABSTRACT FROM AUTHOR]

With the development of new technology for multiple energy applications, household appliances using electricity and other energy sources are becoming increasingly abundant. Users can freely choose the way of energy consumption based on the price of electricity and other energy sources. The phenomenon has become one of the important factors affecting real‐time pricing. This study considers real‐time pricing in the case of multiple energy coexistence on the user side. According to the spatiotemporal coupling of electricity and other energy sources and the relationship between other energy sources and total energy consumption, an optimization model that satisfies the actual situation is established. By using the relaxation theory and dual decomposition technology, a partially distributed algorithm is proposed for multi‐period coupling and finding the optimal energy scheduling scheme for the energy company and each user, so as to maximize social welfare. In addition, under the background of multiple energy coupling, real‐time pricing can guide users to adjust their electricity consumption behaviour vertically and horizontally, change the type of energy, meet energy demand for end users and effectively reduce the peak‐to‐average ratio. Simulation results verify the rationality of the model and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Integrated demand response (IDR) is deemed as an effective tool to balance energy supply and demand. User's uncertain information containing prior uncertain information and posterior uncertain information is a key factor affecting the implementation effectiveness of IDR, but existing studies fail to consider the two types of uncertain information, response risk caused by the uncertain information, and risk appetite comprehensively. Based on the principal‐agent theory of optimal incentive contract under uncertain information and Markowitz's mean‐variance portfolio theory, a new IDR model is established in this paper, and an IDR optimization strategy considering risk appetite under uncertain information is proposed. By proposing the user model considering multi‐dimensional uncertain information and the risk appetite‐based integrated energy service providers (IESP) model based on the principal‐agent theory and Markowitz's mean‐variance portfolio theory, we have achieved effective modelling of the user's uncertain information and the risk borne by IESP. The arithmetic examples have verified advantages of the model in enhancing the accuracy of user's actual response prediction and the superiority of incentive strategies, which is beneficial to reduce the cost of IESPs and enhance the benefit of users participating in IDR. [ABSTRACT FROM AUTHOR]

The movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching‐on/‐off behaviors of EHs in some application scenes with special‐power‐line in low voltage distribution networks (LVDNs), a swarm‐intelligence‐based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users' thermal comforts is researched. A novel bird‐perch‐on‐branch (BPB) ‐based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi‐agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep‐Q‐network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN‐BPB approach is described based on the principle of centralized‐training and decentralized‐execution. Comparative control performances of MADQN‐BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN‐BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment. [ABSTRACT FROM AUTHOR]

Dedicating more attention to renewable energies and power electronic improvements results in increased direct current microgrids (DC MGs) application. However, DC MGs have some challenges with voltage adjustment and power sharing. To do so, a two-layer hierarchical control structure, including a new fully distributed secondary control strategy and conventional primary droop control method, is proposed and employed in this paper to share power and swiftly adjust the voltage accurately. Indeed, a distributed-averaging proportional-integral (DAPI) secondary control strategy is introduced. Another problem in DC MGs is the existence of constant power loads (CPLs), which may result in instability. To overcome the problems caused by CPLs, a term based on the output voltage of CPL is added to the proposed DAPI to prevent instability. The required control inputs are obtained using localized data of the DC bus and their neighbor’s secondary control inputs inspired by cooperative control. Besides, this strategy needs no knowledge of the microgrid topology, which enhances flexibility. For validating the proposed DAPI strategy in DC MGs, an islanded DC MG is simulated in the MATLAB/SIMULINK software. Comparing the results with those obtained from another existing method proves the performance of the proposed DAPI controller under different scenarios of plug-and-play, communication failure, and load changes. [ABSTRACT FROM AUTHOR]

This paper presents a case study of energy flexibility strategies for a building located in the hot climate of Dubai, a type of climate in which energy flexibility has been under-researched. Energy flexibility is changing the routine-consumption profile and deviating from the normal operation of the building by the users to manage the variability in the load profile and cost of electricity. The three flexibility strategies being considered are based on the modulation of the indoor air temperature setpoint profile while considering different marginal costs for electricity. The main objective is to quantify the energy storage flexibility of each strategy and evaluate its impact on the system ramping and load factor. The study was carried out utilizing a grey-box, resistance–capacitance model of the building, which was validated against experimental measurements. This study is the first to use the following five indicators simultaneously: load factor, system ramping, storage capacity, peak-period demand reduction, and cost savings. Combining these indicators helps building facility managers and distribution system operators (DSOs) better understand the implications of implementing a specific flexibility strategy in a building or a group of buildings. When comparing the indicators of each strategy with each other, it was observed that depending on the amplitude of the change in the electricity cost signal during the peak period, a significant cost reduction of more than 25% could be achieved through the implementation of specific flexibility strategies compared with the normal baseline operating condition. [ABSTRACT FROM AUTHOR]

This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting. [ABSTRACT FROM AUTHOR]

Introduction: Basketball, introduced by Naismith as a contactless and indoor alternative to sports such as American football, now frequently involves physical contact among players, challenging the traditional notion. Up to date, a thorough understanding of these contacts and their implications remains limited. This study aims to analyze player contacts, embedding it within overall load monitoring to optimize performance and reduce injury risk. Methods: Using a mixed-method design, video-based observations and quantitative analysis were employed to study contact characteristics during ten professional male basketball matches. Fisher exact tests and chi-squared tests (p < .05) were conducted to examine positional variations across different contact variables. Results: A total of 2,069 player contacts were examined, showing centers had the most contacts at 40.5%, followed by power forwards (19.6%), point guards (17.7%), shooting guards (12.9%), and small forwards (9.3%). Notably, half-court defense (46.1%) and set offense (48.9%) emerged as the primary game phases associated with the majority of contacts across all playing positions. Key play actions leading to physical contact included screening/picking (25.7%), box outs (22.9%), and fights for position (FFP) (18%). Post hoc analyses identified significant associations between centers (32.6%, 5.93) and point guards (21.5%, -1.98) during screening/picking maneuvers. Moreover, the torso/upper body (48.1%) and upper extremities (38.2%) were identified as the most affected contact points, while lower extremities and the head/neck exhibited minimal impact. Additionally, 81.4% (n = 1,684) of contacts resulted in kinematic displacement, whereas 18.6% (n = 385) exhibited no change. Post hoc analyses indicated significant associations of physical contacts against opposing counterparts for each playing position. Discussion: Basketball entails frequent physical contacts across all playing positions, with distinct patterns observed for each playing position. Integrating contact monitoring alongside traditional load metrics offers a more comprehensive understanding of physical demands in professional basketball. Practical implications include the developing of tailored training strategies based on playing position-specific contact profiles and recognizing the physiological and biomechanical impacts of contacts. Future research should consider whether the number of contacts between players has increased over the years, and it should acknowledge the impact of player contacts on performance in basketball in order to refine training strategies and enhance player well-being. [ABSTRACT FROM AUTHOR]

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems. [ABSTRACT FROM AUTHOR]

Renewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid. [ABSTRACT FROM AUTHOR]

This paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is one of the key problem in power systems. ED tends to minimize the fuel/operating cost by optimal sizing of conventional generators (CG). Greenhouse/toxic gas emission is one of the major problem associated with the CG. Emission dispatch (EMD) deals with the reduction of greenhouse/toxic gas emissions by the optimal output of generators. The multi-objective economic emission dispatch (MOEED) problem has been formulated by considering both fuel cost and emission objectives. The main objective is optimization of fuel cost and environmental emissions from the CG in a compromised way. In this paper, CONOPT solver in General Algebraic modeling system (GAMS) has been proposed to find the the optimal solutions for ED, EMD, and MOEED problems of a microgrid. The microgrid consists of a wind turbine generator (WTG), a photovoltaic (PV) module, three CGs, and a battery energy storage system (BESS) option. The proposed algorithm has been implemented in four case studies, including all energy sources, without WTG, without PV module, and without renewable energy sources (RES). To establish the effectiveness of the proposed algorithm, it has been compared with various algorithms. The comparison result shows that proposed algorithm is more effective, novel, and powerful. Finally, result shows the effectiveness of proposed approach to optimize the objective function for all aforementioned case studies and the CONOPT solver in GAMS outperformed all the approaches in comparison. The impact of BESS on the operating/fuel cost of the microgrid has also been presented for ED. Paradigm is changing in terms of demand response in μG. Demand flexibility (DF) model has also been established with consumers demand variation in optimization process. Result with DF shows the reduction in cost and better management from demand side. [ABSTRACT FROM AUTHOR]

Supply-side energy management (SSEM) aims to improve efficiency in operations and strategic planning. Both the cost of generating electricity and the amount of emissions from that generation are minimized in SSEM. It is required to formulate an optimization problem with these two competing goals in order to come up with a compromise. Resolving problems with network reliability caused by peak demand on the electricity system is another goal of SSEM. The ultimate goal of this study is to reduce energy use during peak hours while also cutting down on power losses, generation costs, and pollution from power plants. In this paper all goals of the smart grid system are satisfied and addressed optimally through the use of optimal generator scheduling and an improved demand response technique. To formulate this problem standard IEEE 30-bus system is considered as test boat. The suggested system employs the Cuckoo search method and its most recent variant, adaptive Cuckoo search, to solve a stochastic non-linear optimization problem. The adaptive Cuckoo search approach, when combined with the proposed demand side management strategy, reduces fuel costs by 7.84%, emission dispatch by 16.35%, power losses by 10.31%, and peak hour demand by 15.6%. [ABSTRACT FROM AUTHOR]

Stationary hydrogen energy-based power plants generating electricity to supply high-powered plug-in electric vehicles (PEVs) have recently become popular in renewable energy-based power plants. Besides, in a PEV charging station, various types of powered charge devices can be established such as DC fast chargers or 3.7 kW, 7.4 kW, 11 kW, and 22 kW AC chargers. This paper introduces a demand-side management-oriented optimal charging strategy that includes two stages for PEVs in a hydrogen energy-based microgrid. The paper focuses on two stages to execute an optimal charging of PEVs in compliance with their users' requests and satisfaction and considering the power system loading. It is assumed that there are three types of chargers in the PEV charging station and the users. In the first stage randomly created requests are classified by an ensemble learning classifier method that performs higher performance classification by combining the results from multiple classifiers in a machine learning classification. The second stage schedules the PEVs according to the classification results and users' requests. To test the proposed system, first random requests are created then they are sent to the classifier, and the results of classifiers are scheduled in each other. The demand-side management-oriented charge scheduling and managing strategy which includes the proposed two stages has been compared with non-managed cases. Case study results reveal that the proposed approach provides 52.1% peak load reduction and 72.3% valley filling improvement by the SOS algorithm. The results highlight the advantages of the proposed system in terms of peak reduction and valley filling. [Display omitted] • A new intelligent decision-maker method is proposed for DSM in a microgrid. • Proposed method is developed for a stationary hydrogen energy-based power plant. • The optimal DSM of EVs is investigated with a hybrid ML method. • RUSBoost and SOS Algorithms-based Hybrid Intelligent Decision Maker is developed. • It provides 52.1% peak load reduction and 72.3% valley filling improvement. [ABSTRACT FROM AUTHOR]

This study analyzes energy supply and demand‐side management strategies to increase the renewable energy share in a distributed energy system (DES), which includes demand response (DR) options and different renewable energy sources (RES) and energy storage systems (ESS). To analyze the least costs for increasing the renewable energy share by integrating the DR, RES, and ESS, four scenarios of the DES were generated. Then, a new optimization model was proposed to identify the optimal design and operation strategies, which includes maximizing the profit as an objective function subject to various constraints, such as technical capacity and electricity price. To illustrate the capability of the proposed model, this study presented a case study of the DES for the residential sector of Jeju Island, Korea. The results showed that the optimal scenario, involving the integration of the DR, RES, and ESS, shows 0.115 $/kW hr of the levelized cost of electricity and 10% of renewable energy share, which correspond to 32.7% and 31.5% improvement, respectively, compared to the baseline. Additionally, the practical strategies to meet the Korean target of the high‐share RES were discussed through a sensitivity analysis of major external factors, such as carbon tax, incentives, and the electricity trading price in electricity markets. [ABSTRACT FROM AUTHOR]

Energy storage systems have an important effect on increasing the flexibility of power grids and are necessary to achieve the goals of smart grids. So far, many researches have been conducted in the field of optimal utilization of batteries, but most of them have studied the effects of batteries only on the side of the transmission or distribution network. In the present study, the effect of the installation and optimal operation of energy storage systems on both sides of the transmission and distribution network has been investigated. This paper presents a bi-level optimization model for integrated energy management in transmission and distribution smart grids along with demand management issues. Considering that both proposed models are mixed integer linear programming models, it is difficult to solve them by direct methods. Therefore, a method based on the change of variables and KKT equations is presented to solve these problems, and globally optimal solutions to these problems are guaranteed. Several integrated systems that include transmission and distribution networks are considered to evaluate the proposed model and method, and the results show that the proposed model is efficient and shows the positive effect of modeling energy storage systems on both sides of the network. [ABSTRACT FROM AUTHOR]

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows' food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method's performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system's generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. [ABSTRACT FROM AUTHOR]

Electric load forecasting plays a significant role in electric power systems for numerous applications, in terms of specific time horizons like Demand Side Management (DSM), grid stability, optimal operations, and Longterm strategic planning. However, inaccurate forecasts minimize the power supply safety, affecting the social and economic activities, security, and national defense. Therefore, Temporal Convolutional Attention-based Long Short- Term Memory (TCA-LSTM) is proposed for accurately forecasting electric load using Deep Learning (DL). By including an attention mechanism in an LSTM approach, the proposed technique focuses more on parameters with greater weights. Initially, the power load dataset is employed to evaluate the proposed approach. The obtained data is then pre-processed by utilizing the min-max normalization to reduce the impact of outliers. Then, the Fast Fourier Transform (FFT) technique is performed to extract the dominant amplitude-frequency. At last, the TCA-LSTM is used to accurately forecast the electric load. The proposed TCA-LSTM achieves a better Mean Square Error (MSE) of 0.002, 0.0074, and 1.5047 respectively in Godishala, Warangal, and Vijayawada, compared to the existing techniques namely, Regression Tree (RT), Principle Component Analysis with Recurrent Neural Network (PCA-RNN), and Artificial Neural Network (ANN). [ABSTRACT FROM AUTHOR]

Gossen's First Law describes the law of diminishing marginal utility. This paper aims to further verify the proposed hypothesis that Gossen's First Law also holds in the modeling for Demand Side Management (DSM) with a thorough heat pump case study. The proposed hypothesis states that in general the complexity-utility relationship in the field of DSM modeling could be represented by a diminishing marginal utility curve. On the other hand, in data based modeling, when utilizing a large dataset for validation, the data integrity is critical to the reliability of the results. However, the absence of partial time series data may occur during the measurement due to missing sensors or IT related issues. In this work, an extensive real-world open dataset of a ground source heat pump is utilized for the case study. In the raw data, one key variable namely the flow rate is missing. Thus, three different algorithms based on machine learning and deep learning architectures namely Random Forest (RF), Long Short-Term Memory (LSTM) and Transformer are applied to predict the flow rate by utilizing an open loop forecasting. The raw data are first pre-processed with a time interval of one hour and then used for training, validation and forecast. Furthermore, a modified persistence model as the baseline is also defined. The predicted flow rate using LSTM yields the lowest error of 7.47 % nMAE and 10.56 % nRMSE respectively. The forecast results are then utilized in the following step of modeling of a heat pump use case. With the introduced quantification method for complexity and a modified version for utility, we further verify the proposed hypothesis with a longer time horizon of 7 days. [ABSTRACT FROM AUTHOR]

This article delves into the dynamics of power system and microgrid planning, emphasizing the emerging significance of energy hubs. The study introduces a pioneering multi-objective model that integrates economic considerations and microgrid pollution mitigation. The focus is on renewable sources such as wind and solar, coupled with advanced energy storage systems like E-fuel. Sophisticated probabilistic modeling techniques address uncertainties in renewable resources, complemented by a demand-side management program for cost reduction during peak hours. Transforming into an optimization problem, an innovative algorithm inspired by African vulture foraging behaviors provides a distinctive approach to energy landscape optimization. In a comparative analysis with established models, this approach demonstrates proficiency in simulating market behavior and providing optimal strategies for profit maximization. In a simulation analysis of four scenarios, the impact of risk assessment, a novel electronic fuel storage market, and an innovative energy storage solution on operational costs and pollution emissions is scrutinized. Notably, the proposed method excels in Case 4, showcasing a significant reduction in both operational costs and emissions. Case 4 demonstrates a substantial reduction in operational costs to $1745.732 and pollutant releases to 10753.621 kg, outperforming existing models with lower operational costs and pollutant releases. [ABSTRACT FROM AUTHOR]

Summary: The past decade has brought prolific developments to power systems that range from large‐scale renewable integration to digital twins. Modern power systems consist of highly variable generation clusters. Hence, monitoring and prediction of power generation has become a vital part of today's power systems. On the other hand, detailed real‐time information of power usage is also a key parameter for the control algorithms used for the demand side management. With this information flow, power systems become smart grids which offer improved service and enable many advanced operations. In this regard, the home area network (HAN), which collects the power consumption/generation of household devices, plays a key role. So far, a number of technologies such as ZigBee, Z‐Wave, and Power Line Communication (PLC) have been considered for the HAN. Yet, widely used wireless local area network (WLAN) technology, Wi‐Fi, was not identified as a prominent candidate due to the limitations in its latency and reliability. However, with the introduction of the IEEE 802.11ax standard, Wi‐Fi 6 was developed, and it demonstrates guaranteed latency and reliability for its users. Thus, with its widespread deployment in households, Wi‐Fi is becoming a natural candidate for HAN. In this article, we present a comprehensive HAN protocol based on Wi‐Fi 6 which can achieve guaranteed latency and reliability for HAN users. The results show that when an efficient resource allocation mechanism is in place, using Wi‐Fi for HAN does not adversely affect the other Wi‐Fi users in the normal WLAN. [ABSTRACT FROM AUTHOR]

Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs. [ABSTRACT FROM AUTHOR]

We consider an energy system with n consumers who are linked by a Demand Side Management (DSM) contract, i.e. they agreed to diminish, at random times, their aggregated power consumption by a predefined volume during a predefined duration. Their failure to deliver the service is penalised via the difference between the sum of the n power consumptions and the contracted target. We are led to analyse a non-zero sum stochastic game with n players, where the interaction takes place through a cost which involves a delay induced by the duration included in the DSM contract. When n → ∞ , we obtain a Mean-Field Game (MFG) with random jump time penalty and interaction on the control. We prove a stochastic maximum principle in this context, which allows to compare the MFG solution to the optimal strategy of a central planner. In a linear quadratic setting we obtain a semi-explicit solution through a system of decoupled forward-backward stochastic differential equations with jumps, involving a Riccati Backward SDE with jumps. We show that it provides an approximate Nash equilibrium for the original n-player game for n large. Finally, we propose a numerical algorithm to compute the MFG equilibrium and present several numerical experiments. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

With a high percentage of distributed new energy sources connected to the power system, the power grid needs to reserve a larger margin to deal with the uncertainty of renewable energy outputs, leading to an increase in the cost of controlling the margins for the safe operation of the power grid. In order to reduce costs and increase efficiency, a quantitative assessment of new energy output uncertainty is needed. In this paper, a quantitative assessment method of new energy output uncertainty based on the prediction error is proposed, which makes use of a graph database to efficiently obtain massive new energy historical data, uses the clustering in quest (CLIQUE) algorithm to cluster the new energy historical data, and calculates the renewable energy real power confidence interval based on a given new energy power prediction, taking account of the impact of prediction errors caused by the new energy uncertainty and realizing the quantitative description of new energy output uncertainty. Finally, the method is calculated and analyzed together with the actual example data to verify the practical effect of the method. [ABSTRACT FROM AUTHOR]

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry's pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer's cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context. [ABSTRACT FROM AUTHOR]

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs. [ABSTRACT FROM AUTHOR]

As electric hot water cylinders (HWCs) have a large capacity for thermal storage, they are well-suited for Demand Side Management (DSM). This paper compares different methods of HWC temperature control and presents a methodology to assess the amount of thermal storage available in HWCs for demand side management based on use behaviour in different household types. Simple stochastic methods for domestic hot water (DHW) demand prediction were employed to design a smart controller that produced lower rates of unmet DHW demand and higher available storage than setpoint and ripple controllers. The average storage available for DSM from the use of this smart controller is predicted to be between 3.63 and 7.20 kWh per household. These results indicate the use of HWCs for thermal storage is a low-cost viable option for peak-shaving of power system load and could decrease power system greenhouse gas (GHG) emissions in countries such as Aotearoa New Zealand, where GHG-emitting electricity generation is primarily used to meet peak loads. [ABSTRACT FROM AUTHOR]

In recent years, energy management (EM) has become a vital tool for the planning of various energy sources in the microgrid. The EM scheme helps in the effective planning of various energy sources so that a proper balance between generation and load demand can be maintained. In this aspect, different management schemes have been developed to effectively plan various sources. In this work an interactive class topper optimization (I-CTO) based energy management scheme for an interconnected microgrid considering renewable energy sources, battery storage systems, demand side management, etc. is presented. The proposed scheme aims to optimally plan different energy sources in the microgrid so that the generation and emission cost may be minimized. To achieve this objective of having an optimal distribution of generation among different energy sourest, an objective function is formulated. This developed objective function is minimized using the proposed I-CTO scheme while maintaining some of the operational constraints encountered in real-time scenario. The reduced objective corresponds to the optimal distribution of generation among different energy sources which would help to fulfill the load demand with less generation and emission cost. To test the effectiveness of the proposed schemes and show their supremacy over some existing methods, a comparative study is presented using a numerical test example and CEC-2020 benchmark function. [ABSTRACT FROM AUTHOR]

The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30–40% of customers participated in the DSM curriculum, the generation cost was reduced by 10–13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy. [Display omitted] • Restructuring of load demand to study impact of DSM. • Comparison amongst different CEED types with and without DSM. • Effects of different grid participation strategies for economic generation of microgrid. • Effects of different grid pricing strategies for economic generation of microgrid. • Superiority of proposed CSAJAYA algorithm compared to published results. [ABSTRACT FROM AUTHOR]

In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To solve these issues, this study suggests non-dominated sorting genetic algorithmII (NSGA-II) for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources. The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions. TOU-based DRP is used, especially considering the time of outages along with the time of peak loads and prices, to enhance the reliability of MG and reduce costs and emissions. [ABSTRACT FROM AUTHOR]

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value. [ABSTRACT FROM AUTHOR]

This paper suggests day-ahead scheduling of microgrid with and without demand side management considering hydrogen storage and plug-in electric vehicles. This is a highly constrained mixed integer nonlinear programming problem which is solved by using quasi-oppositional fast convergence evolutionary programming (QOFCEP) technique. The concept of the quasi-opposition-based learning is incorporated in fast convergence evolutionary programming (FCEP) to improve the efficiency and quality of the solution. QOFCEP employs quasi-oppositional based learning (QOBL) for population initialization and generation jumping. The studied microgrid comprises three diesel generators, one mini-hydro power plant, two wind turbine generators, two solar PV plants, one hydrogen storage system and plug-in electric vehicles. Simulation outcomes of the suggested QOFCEP technique have been compared with those obtained by FCEP and differential evolution (DE). It is seen from numerical results that the cost obtained with DSM is about 0.81% lower than the cost obtained without DSM. It is also seen that the cost obtained from QOFCEP is about 0.52% and 0.78% lower than the cost obtained from FCEP and DE, respectively. The suggested QOFCEP technique has the ability to bestow superior-quality solution. [ABSTRACT FROM AUTHOR]

Demand‐side flexibility or demand response (DR) has long been recognized as a potential source of balancing capacity. The charging of electric vehicles (EVs) is a known source of flexible capacity, and the vast amount of charging capacity available can be utilized for valuable applications, including ancillary power markets, by controlling the charging sessions according to the needs of the power system. One identified and unexplored potential market for EV DR capacity is the Fast Frequency Reserve (FFR) market for the Nordic system area. This article proposes and demonstrates that a home charger can participate in this extremely fast reacting ancillary service market with only software modifications made to the charger. The biggest challenge in the FFR market is the response time requirement for capacity activation. When a certain frequency limit is exceeded, the activation must happen within 0.7 s in the worst case. In this study, a laboratory test setup was constructed to test the capabilities of the FFR‐enabled EV charger. The tests were conducted by building a laboratory microgrid capable of changing the grid frequency. Additionally, an economic feasibility study was carried out to evaluate the business potential of the concept in Finland. The economic study included an analysis of the concept's potential with different levels of DR service availability. The laboratory tests demonstrated the FFR capabilities of the charger and as a final outcome of the research, the Norwegian transmission system operator (TSO) approved the tested charger as a type‐prequalified FFR‐providing entity. The economic study revealed that the approach has good business potential, primarily because of the almost non‐existent cost side even if the availability rate decreases significantly. [ABSTRACT FROM AUTHOR]

The fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy‐intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy‐intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank‐Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance‐constrained theory of a reserve coefficient to describe time‐variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118‐bus system show that the energy‐intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power. [ABSTRACT FROM AUTHOR]

By classifying loads as elastic or inelastic and restructuring the load demand model, demand side management (DSM) may help bring down the distribution system's operational costs. One way to accomplish this goal is to shift the time of day that the flexible loads are used to one that has a lower utility cost per unit of use. Using renewable energy sources (RES), and fossil fuel-powered generators in grid-connected mode, this article implements a bi-level optimization technique to reduce operational costs. First, the load model is reorganized in accordance with the DSM involvement level. The second stage involves considering the revised load demand model and percolating an ideal allocation for distributed generators (DGs) to reduce the microgrid's generation costs. This study made use of a novel hybrid swarm intelligence algorithm developed for the purpose of optimizing power systems, which has proven effective in the past for a wide range of optimization problems. All types of grid involvement and price methods were explored for producing cost without considering DSM. When 10–30% DSM involvement was considered, the numerical findings showed a noticeable decrease in generating cost. A detailed statistical and sensitivity analysis of the proposed hybrid optimization tool also corroborated about the robustness and preciseness of the same. [ABSTRACT FROM AUTHOR]

Time‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand. [ABSTRACT FROM AUTHOR]

The increasing interactivity between energy carrier networks such as electrical, natural gas (NG), and heating can be appropriately used for improving the operation optimality along with overcoming some challenges of those systems. With such a view, this paper studies the practical issue of power transmission lines congestion management in integrated multicarrier energy systems (MCESs) and makes a comparison with the only electrical operation mode. The MCES consisting of electrical and NG network infrastructures with exchanged energy between them is modelled by considering the several local energy hubs and their electrical and heat loads. Rescheduling of energy sources (power plants and gas wells) and demand side management in both electrical and NG networks have been utilized as the two important tools in the developed congestion alleviation framework. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to a remarkable reduction in the congestion management cost index. Implementing the proposed congestion management framework on an assumed test MCES with a combined 39‐bus New England power system and 20‐node Belgium NG system demonstrates its effectiveness and applicability for the system operators. The issue of power transmission lines congestion management in integrated multi‐carrier energy systems (MCESs) has been studied. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to reduction in the congestion management cost. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers' voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs' chargers. Here, a model for encouraging industrial loads to dynamically invest in implementing the conservation voltage reduction (CVR) strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to the fact that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. [ABSTRACT FROM AUTHOR]

Northeast China has been facing a severe power shortage situation. Since September 2021, "power rationing" events occurring in many places in the three provinces of northeast China have been causing inconvenience to people's production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, and the pressure on coal and electricity will further increase. In Heilongjiang Province, due to coal capacity control, limited production led to the high price of thermal coal; wind power photovoltaic output fluctuations, the epidemic, and other reasons also resulted in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of people's daily electricity consumption, rational distribution of power generation plans, and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of the backpropagation (BP) neural network prediction model so that the final prediction results have the advantages of the BP neural network prediction model and Markov model. In addition, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately. [ABSTRACT FROM AUTHOR]

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is widely utilized in smart grid for its reliable features, flexibility, and cost benefits that it offers to customers on reduction of the energy bill. In the smart grid, demand response aggregator, power customers, and utility operator all strive to increase their individual profits. However, it is extremely challenging to guarantee profits for all of the candidates simultaneously. In this paper, these criteria are employed to execute a problem with multiple objectives by combining the concept of DSM and dynamic economic emission dispatch considering uncertainties of renewable energy sources (RESs). The uncertainties of RESs can bring adverse effects on the grid operator. Thus, Weibull probability distribution function and lognormal probability distribution function are used to model the uncertainties of wind speed and solar irradiation, respectively, in this paper. Further, the multi-objective improved DSM scheme is optimized by Class Topper optimization algorithm. The objective here is to optimally schedule load demand and generation pattern simultaneously to improve load factor, minimize electricity bill and maximize the profits of all candidates of the day-ahead electricity market simultaneously. Error in the load forecasting model may lead to growing operational cost of energy generation. In this paper, a machine learning model using linear regression is used for short-term load forecasting (STLF) to forecast day-ahead load demand. The simulation results show the importance of the improved DSM scheme and the advantage of STLF, which further help to improve the economy and efficiency of the smart grid. [ABSTRACT FROM AUTHOR]

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP's storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units. [ABSTRACT FROM AUTHOR]