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With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis. [ABSTRACT FROM AUTHOR]

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

Non‐intrusive load monitoring (NILM) is an important technology for deeply mining consumers' internal electricity consumption information, which can improve the level of awareness of the load and significantly improve the demand‐side management capability of the smart grid. For a long time, traditional NILM faces the challenge of huge data, long training time and low accuracy. To address this issue, this paper proposes an adaptative lightweight seq2subseq model. The model adaptively determines the optimum window length of the active power segment to extract the appliance characteristics and thus reduces the redundant data to construct the appliance‐level‐seq2subseq model. The proposed model significantly improves the ability to extract and utilize information. At the same time, transfer learning is used to ban the training of appliance models with similar characteristics, further reducing model complexity and training time. The experimental results based on the REDD dataset verify the method's effectiveness. Compared with the seq2seq and seq2point models, the proposed model achieved better accuracy and F1 score, up to 98.10% and 90.29%, respectively. At the same time, the proposed method is further verified on UK‐DALE, which proves that the proposed method has good portability and robustness. [ABSTRACT FROM AUTHOR]

Recently, many industrial users have spontaneously built energy storage (ES) systems for participation in demand-side management, but it is difficult for users to benefit from participating in demand response (DS) because of the expensive costs of ES construction. Therefore, this study proposes a cloud ES (CES) architecture that can reduce these costs by utilising users' complementary load characteristics and the scale benefits resulting from large-scale construction of ES equipment. Considering the DR and the uncertainty of the user load, this study applies two-stage robust optimisation to solve for the optimal configuration of CES. The proposed optimisation model is verified using the load data of an industrial park in Jiangsu Province, and the results clearly indicate that the proposed CES can be more beneficial than self-built distributed ES during the warranty period. The robust configuration results can be applicable even when the load changes within the maximum and minimum range. [ABSTRACT FROM AUTHOR]

With the access of renewable distributed generations and the uncertainty of demand-side response, the operation states of the active distribution network (ADN) are uncertain. This study proposes a novel optimal strategy taking active management costs of the source--network--load as the goal and considering the site selection and the incentive price for the responsive load to optimise the ADN. To deal with uncertainties, a chance-constrained optimisation model is established and the Monte Carlo sampling method is used. Then, the uncertain model is settled by the improved backtracking search algorithm, which uses an adaptive mutation scale factor. Finally, the practicability of the proposed strategy and the performance of the algorithm are verified by the modified IEEE 33-bus distribution system and an actual distribution system in China. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses several articles within the issue on topics including voltage control for micro-grids; use of voltage control in the demand side management; and stability issues in the voltage control.

Recently, various distributed energy resources are significantly integrated into the modern power systems. This introduction of distributed energy resource‐rich systems can cause various power quality issues, due to their uncertainties and capacity variations. Therefore, it is crucial to establish energy balance between generation and demand to improve power system's reliability and stability and to minimize energy costs without sacrificing customers' comfort or utility. In this regard, power system flexibility concept is highlighted as a robust and cost‐effective energy management system, especially on the demand side, to provide consumers' demands with an acceptable level of power quality. Accordingly, here, a comprehensive review of recent developments in the power system flexibility and demand‐side management strategies and demand response programs are provided to include mainly classifications, estimation methods, distributed energy resource modelling approaches, infrastructure requirements, and applications. In addition, current research topics for applying power system flexibility solutions and demand‐side management strategies based on modern power system operation are deliberated. Also, prominent challenges, research trends, and future perspectives are discussed. Finally, this review article aims to be an appropriate reference for comprehensive research trends in the power system flexibility concept in general and in demand‐side management strategies and demand response programs, specifically. [ABSTRACT FROM AUTHOR]

In the present study, the economic model predictive control (EMPC) scheme is presented for the time of use tariff application of photovoltaic and diesel generator backup system, which is connected to the grid‐connected microgrid. Through the use of the closed‐loop control system, the prior open‐loop optimal control is enhanced. Minimizing the grid energy and fuel costs by evaluating the fuel level restrictions in the diesel tank is the main goal of this study. To accommodate the restrictions among controllable variables, this control method fulfils the load requirements. To gain the benefits of feedback and predictions, optimal power scheduling is modelled as a control problem. Furthermore, Blockchain technology is applied to secure interchanged information in the grid to avoid manipulating data by attackers. Specifically, the analysis is divided into two scenarios. The first one takes place in the alternative style when a blackout happens between [7 am and 6 pm] h and the other occurs in a grid energy mode when the network is available over 24 h. Energy performance, cost savings, and daily revenue have all been improved by EMPC. As a result, the daily energy savings are up to 52%, whereas diesel energy does not go over 85%. [ABSTRACT FROM AUTHOR]

Considering increasing distributed energy resources and responsive loads in smart grid paradigm, this study proposes a new approach for robust hourly energy scheduling of distribution systems at the presence of severe uncertain renewable energy sources (RES). Wind and photovoltaic power generations are considered as the RESs. The aim is to minimise the total energy procurement cost, while considering the participation of RESs, by their optimal allocation in the network. The inherent uncertainty of RESs is handled via information gap decision theory. One of the features of the proposed model is to consider the impact of demand response and energy storage system as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the proposed model handles the uncertainty of multiple RESs in a way that maximum tolerable uncertainty of RESs is achieved for a given worsening of total energy procurement cost. The proposed model is formulated as a mixed integer nonlinear optimisation problem and is implemented in general algebraic modelling system environment. The model is applied on the IEEE standard 33-bus radial test system, and the obtained results substantiate that the utilisation of ESS and DR can reduce the impact of RESs' uncertainty on the energy cost. [ABSTRACT FROM AUTHOR]

The immeasurable energy‐saving potential of industrial parks and commercial buildings with unprecedented growth in scale and quantity can be well exploited by load disaggregation, a promising technique for demand‐side refinement management. However, traditional load disaggregation studies mainly focus on residential users and neglect the collection and analysis of industrial and commercial loads. This study employs a simulated response model of versatile load categories with voltage‐dependent load functions to overcome the unavailability of industrial and commercial datasets. Specially, a weighting factors allocation method considering probability statistics of power consumption distribution characteristics is proposed to construct a typical daily multi‐type load aggregation model. Furthermore, an intelligent load disaggregation strategy is developed based on a multi‐channel neural network to constructively improve multi‐feature tracking and convergence efficiency. Finally, the simulation results of the detailed study case confirm the tractability and effectiveness of the proposed approach to carry out load identification among multi‐type users, with an accuracy of 94.9% and errors of individual categories controlled between 3.32% and 10.686%. Moreover, additional interference tests demonstrate that the developed model has superiority over common load disaggregation structures, with better tolerance to exceptional voltage fluctuation and noise. [ABSTRACT FROM AUTHOR]

Due to the rising infiltration of renewable energy sources, it is indispensable to investigate its brunt on the optimal power generation scheduling. However, the highly intermittent nature of renewable energy sources and their higher rate of outages may harm the entire grid. This work recommends fast convergence evolutionary programming (EP) with a time-varying mutation scale (FCEP_TVMS) for solving fixed head hydrothermal scheduling incorporating pumped-storage-hydraulic unit with demand-side management considering the uncertainty and outage of renewable energy sources. Simulation outcomes of the test system have been matched up to those acquired by fast convergence EP, colonial competitive differential evolution and heterogeneous strategy particle swarm optimisation. It is seen from the comparison that the recommended FCEP_TVMS technique can give a better-quality solution. [ABSTRACT FROM AUTHOR]

Owing to the important role of the power system in modern societies, its resilience against natural disasters has become a top priority for power system operators and planners. Moreover, in recent years, due to the increasing number and severity of weather-related events, several operational solutions have been proposed to increase the resilience of power systems. For instance, in the distribution part of power systems, dynamic micro-grid (MG) formation can enhance resilience by means of distributed energy resources (DERs) when the main grid is unavailable. Following this concept, the capabilities of electric vehicles (EVs) such as vehicle-to-grid make it possible to use the energy stored inside them to restore critical loads. Therefore, the aggregation of EVs in the distribution system can be seen as a DER from the system operator viewpoint, and operators can benefit from this limited energy resource in MG formation. This study uses two types of EV aggregation for this purpose, i.e. a public parking lot and a residential parking. To improve system performance when using these resources, two steps should be taken: (i) employing a master–slave control technique in each MG and (ii) using a demand-side management programme. [ABSTRACT FROM AUTHOR]

This study presents a new solution to cope with the intermittent nature of renewable generations (RGs) and facilitate the integration of RGs in the smart active distribution network. Electric spring (ES) as one of the most influential solutions in demand-side management is proposed as a flexible resource for flexible operation of grid-connected microgrid against other sources of uncertainty such as forecasted load demand and energy price as well as the RGs output. This innovative approach has been considered in the context of a stochastic problem by presenting the static model of ES for the first time. The modelling of uncertainties is done by the roulette wheel mechanism as a scenario generation process and backward method for reducing the number of scenarios. In the proposed optimisation problem, the objective function is minimising the operating cost and voltage deviation as well as maximising system flexibility, subject to the AC power flow, ES and RGs constraints and technical system limitations. Finally, the proposed solution is tested on 33-bus IEEE test system by the general algebraic modelling system software. The case studies demonstrate the efficiency of the proposed ES model in different simulation and experimental cases in providing flexi-renewable microgrid. [ABSTRACT FROM AUTHOR]

In this study, the thermostatically controlled loads (TCLs) are employed to track automatic generation control signal through changing the temperature setpoint offset in smart grid. The costs minimisation problem is formulated for the power company in demand-side energy management. The costs are caused by the imbalance of supply and demand and consumer discomfort, which achieve a tradeoff by using a weighting coefficient. The forward method and the tracking-differentiator are applied to achieve the costs optimisation. In the simulation, the root-mean-square error (RMSE) and the costs are measured by changing the weighting coefficient. Simulation results demonstrate that the RMSE and the costs of the power company are increased with the weighting coefficient and the tracking error can be reduced. Further, the impact of dividing TCLs into clusters on RMSE and costs is considered. It is shown that the RMSE and the costs are increased with the number of groups. [ABSTRACT FROM AUTHOR]

The existing transmission charging methods like long-run incremental cost give forward-looking charges which reflect the impact of system congestion on required network investment, but the flexibility of demand is not considered. This study proposes a novel transmission charging method which takes into consideration the demand response (DR). The proposed method could reflect: (1) how DR influences the short-run congestion management cost and long-run investment cost; (2) how DR influences the transmission charges. To promote the effect of DR, an approach is proposed to determine the optimal nodes for the implementation of DR based on the available transfer capability and congestion cost. Through the approach, different plans for the implementation of DR will be produced in different demand levels. The optimal node determination approach not only makes DR more targeted for the system congestion but also improves the effect of the proposed transmission charging method. The proposed method is applied to the IEEE 14-bus system and a practical system in Western China. The results indicate that the proposed charging method can offer consumers with economic incentives to alleviate congestion, especially when the DR implemented on the optimal nodes, and thus delay the network reinforcement investment. [ABSTRACT FROM AUTHOR]

This work presents a cooperative demand-side management (DSM) scenario in a low-voltage network considering a context of liberalised electricity markets. The authors show that introducing an additional inter-supplier cooperation mechanism among consumers enhances a better use of the flexibility consented by each individual, hence aiming at reaching a global optimum instead of optimising the costs of a few ones. To that end, a real-time pricing (RTP) scheme is explored based on cost functions differentiated in both time and consumption level that should reflect the true energy cost. The authors apply to each consumer a commodity cost function shared among the set of cooperative users of its respective supplier as well as one common network cost function shared by all cooperative users of all suppliers in the considered network. Each individual runs, through its smart meter (SM), a decentralised optimisation algorithm defining an energy consumption schedule for a set of flexible appliances (FAs). The mechanism the authors propose ensures that a fair cost distribution between all users is achieved by reaching the Nash equilibrium. To assess their proposition, they confront it to intermediate consumption strategies through a benchmark. The results confirm that their inter-supplier cooperation mechanism always leads to the minimum total cost. [ABSTRACT FROM AUTHOR]

Expansion of cogeneration technologies, such as combined heat and power units, has boosted the growth of multi-carrier energy systems. A two-step method is presented to enable residential demand response (DR) programmes in the multi- carrier energy systems. In the first step, the energy management system at each home solves an optimisation problem to achieve the desired energy cost and demand schedule for the customer according to received price signals. In the second step, the system operator revises the demand scheduling by running another optimisation problem to minimise the total electrical losses, subject to the operational characteristics of electrical and natural gas systems. In order to persuade customers to participate in the DR programmes, it is guaranteed that the resulted cost of the second step is not more than the desired cost of the customer in the first step. Results of applying the proposed method, incorporating different penetration levels of customer participation in a time-of-use programme is studied in a test energy system. Simulation results verify the effectiveness of proposed method in minimising the total electrical losses, improving the operational characteristics of the energy system as well as providing customers' utilities. [ABSTRACT FROM AUTHOR]

In a smart grid environment, the utilities, which electric power distribution companies with different pricing strategies, are interested in demand side management (DSM) schemes to achieve reduction in peak demand. Utilities provide additional benefits and encourage the consumers to have direct interaction with the grid by actively participating in DSM programs. An intelligent residential load management system (IRLMS) is proposed for consumers to attain reduction in electricity bill and avail more incentive from the utility by responding to their DSM schemes. The IRLMS attempts to reduce the electricity bill and maintain the total load under maximum demand limit by scheduling the schedulable loads while considering the operating dynamics of non-schedulable loads. The IRLMS scheduler is built with two different techniques, namely priority-based scheduling algorithm and optimisation-based scheduling algorithm for different utility tariff methods such as flat rate tariff and real time pricing, respectively. The IRLMS model is implemented in MATLAB and the results illustrate significant benefits for both consumers and utility. [ABSTRACT FROM AUTHOR]