This study aims to address critical gaps in Smart Renewable Energy Communities research by examining the potential of it powered by real-time metering and control technology to utilise Peer-to-Peer transactions. It examines the evolution of regulatory frameworks and economic models for individual and Smart Renewable Energy Communities self-consumption, and their impact on grid stability and operations. It introduces the High Street Electricity Market model that allows end-users to participate in an alternative marketplace such as a Peerto-Peer marketplace while retaining their existing retail relationships. The High Street Electricity Market model, designed to comply with European Union regulations, has been implemented and demonstrated in a live setting within a residential community in Dublin. The paper focusses on Peer-to-Peer contract negotiation, regulatory alignment, and fair competition between Smart Renewable Energy Communities, retailers and individual selfconsumption. Out of four scenarios presented in this paper, Scenario 4 presented in Table 5 outlined a balanced competitive environment created by applying a full 100 % discount on the passthrough tariff for individual selfconsumption, and a 55 % discount for Smart Renewable Energy Communities self-consumption at the same cost saving of 82 %. This strategy maintains market stability and offers a practical solution for ensuring fairness between individual users and Smart Renewable Energy Communities. It highlights the capacity of Peer-to-Peer to add value to grid operations by controlling community self-consumption, in real-time. Furthermore, it explores the market potential of Smart REC and emphasizes the need for regulatory and market design changes to encourage broader adoption. The findings underscore the feasibility of High Street Electricity Market model in promoting community engagement in the energy transition and advocate for further research to develop a robust model for discounts in passthrough charges and comprehensive licensing protocols for Peer-to-Peer marketplace operators to enhance the realization of societal value by Smart Renewable Energy Communities.

In the future power grid, a local electricity market (LEM) with renewable energy sources and a smart grid will play a key role. Consumers, prosumers and small distributed energy resources on the other hand, play an important role in local energy transactions. In the LEM under study, the system costs are decreased and prosumers are given additional incentives for exporting the power in the grid or LEM. This paradigm is designed to boost the willingness and engagement of customers in local electricity market, so empowering them to maximize their revenues. The suggested work combines customer segmentation and operating envelope to achieve both system cost reduction & customer profit maximization objectives. The optimization challenge also takes into account the performance of the system with and without LEM. A cost comparison of the system between various cases and scenarios yields positive findings. The impact of incentives on prosumers' gross revenue is also examined. A system with 30 local participants makes up the test case system. By comparing the results of various scenarios and cases, minimum cost reduction of 0.2 % and maximum cost reduction of 25.8 % is achieved. While analyzing profit of players, incentive contributes a minimum 7 % and maximum 8 % in total revenue.

This paper proposes a system, CosyGrid, to achieve the objective of Renewable Energy Communities (RECs), such as maximizing self-consumption, through Peer-to-Peer (P2P) trading. In P2P trading, each order from the peer is matched with an order (or orders) from any other peer (peers) in the same REC marketplace. But it is often not possible to fill all orders of peers with matching orders from the other peers. Those orders from the peers that remain unfilled at the start of the delivery period but cannot be cancelled (i.e. are not flexible) are matched with the peer's retailer as per the supply agreement, based on actual volumes imported or exported. In this proposed design for CosyGrid, contracts are instead settled based on contracted volumes of consumption and production and the difference (imbalance) between actual and contracted volumes, and the retailer contracts for unfilled orders are settled at a higher spread between buy and sell price than the supply agreement. These adapted supply agreements allow for stronger p2p price signals and are referred to in CosyGrid as Framework agreements. But the resulting difference between supply and Framework agreements can negatively impact the effectiveness of P2P trades to achieve the REC's objective. To minimize the impact, CosyGrid settles imbalances such as to ensure that the aggregated value in Framework agreements is the same as the aggregated value in Supply agreements. A price per unit of imbalance called the Imbalance price, is calculated, and applied to every end-user proportional to the individual imbalance. The proposed approach will incentivize end-users to respond to price changes and motivate them to use accurate forecasts for their orders, which will further improve the effectiveness of P2P trading.

The growing demand for electricity in Europe has increased the need for a more flexible and sustainable power system. In recent years, Demand Response (DR) has emerged as a promising solution to meet this need, by providing an opportunity for residential and smaller commercial consumers to actively participate in the electricity market. This research paper investigates the potential for DR among the residential community and small commercial electricity consumers in Europe and identifies the technological barriers and drivers that impact consumer engagement with DR programs in Europe. The different DR opportunities are identified and validated at the six different demo sites in three different European countries: Ireland, Italy, and Spain. Four specific objectives of the DR applications are identified: 1). DR to avoid curtailment of renewable energy generators by the electricity grid operator 2). DR to optimize the energy efficiency of a central or district heating system 3). DR to limit the maximum power imported on a grid connection shared by several consumers 4). DR to maximize the level of self-consumption in a Renewable Energy Community (REC). The application of DR objectives will be implemented using a state-of-the-art community based Peer2Peer energy market platform and Digital Twin optimization engine together with the smart hardware infrastructure to enhance community engagement in DR. Finally, this paper suggests the improvements required in the Building energy performance standards and the Electricity Market design regulations to enhance the participation of the community in DR.

An indeterminate and variable nature of renewable energy sources like solar photovoltaic, wind power, load consumption, electric vehicles trips and market spot prices, make the operation and control of energy management system quite complex. Also, it is expected that the system should be consistent and resilient in case of extreme events like faults, hurricanes etc. This paper has used the risk based optimization strategies considering uncertainty of aforementioned parameters to minimize the operational cost of the aggregator. A 13-bus practical distribution system with 15-scenarios (03-scenarios as extreme events with high impact) are considered as a test system. WCCI-2018 award winning, Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO) computational intelligence method has been used to solve this problem. The comparative analysis of EVDEPSO with most popular Differential Evolution (DE) method shows that it provides better solutions than DE method.

The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.

An Artificial Neural Network (ANN) is one of the most powerful tools to predict the behavior of a system with unforeseen data. The feedforward neural network is the simplest, yet most efficient topology that is widely used in computer industries. Training of feedforward ANNs is an integral part of an ANN-based system. Typically an ANN system has inherent non-linearity with multiple parameters like weights and biases that must be optimized simultaneously. To solve such a complex optimization problem, this paper proposes the Levy Enhanced Cross Entropy (LE-CE) method. It is a population-based meta-heuristic method. In each iteration, this method produces a "distribution" of prospective solutions and updates it by updating the parameters of the distribution to obtain the optimal solutions, unlike traditional meta-heuristic methods. As a result, it reduces the chances of getting trapped into local minima, which is the typical drawback of any AI method. To further improve the global exploration capability of the CE method, it is subjected to the Levy flight which consists of a large step length during intermediate iterations. The performance of the LE-CE method is compared with state-of-the-art optimization methods. The result shows the superiority of LE-CE. The statistical ANOVA test confirms that the proposed LE-CE is statistically superior to other algorithms.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

Within the MicroGrid environment, the Energy Resource Management (ERM) problem becomes highly complex due to the uncertainty related to the Renewable Generation (RG) such as Photovoltaic power generation (PV), Electric Vehicle (EV) trip with Grid to Vehicle (G2V) or Vehicle to Grid (V2G), Energy Market price and load demand with Demand Response (DR) programs. Each of these issues should be tackled while optimizing revenues and reducing the running costs of Virtual Power Player (VPP) that collects each of these types of energy resources from the MicroGrid. This article presents a new hybrid optimization algorithm called "Hybrid Levy Particle Swarm Variable Neighborhood Search Optimization" (HL_PS_VNSO) to solve the ERM problem. Its key aspect is the hybridization of the Particle Swarm Optimization (PSO) and the Variable Neighborhood Search Optimization (VNS) algorithm with the enhanced step length using Levy Flight. The effectiveness of the proposed approach is measured by a 25-bus MicroGrid with 500 uncertain scenarios of the aforementioned uncertainty. The results of HL_PS_VNSO are compared with the most advanced optimization algorithms. The findings show that HL_PS_VNSO's results are superior for the Average Ranking Index (A.R.I) and Ranking Index (R.I). For effective comparative analysis of algorithms, the traditional statistical method called One-way ANOVA Tukey Analysis is used. The results from this analysis also prove the superiority of HL_PS_VNSO over the most advanced optimization algorithms.

The day-ahead Energy Resource Management (ERM) problem with the aim to backing the functioning decisions of Virtual Power Player (VPP) in the microgrid environment. The aim of the VPP is to manage the available distributed energy resources as practically as possible with the objective of minimizing the operational cost and maximizing profits by reducing the need to buy energy from the external supplier or electricity market at high prices. The day-ahead ERM is executed the day before the energy trades are due. Typically, the considered trades periods are one-hour corresponding to 24 scheduling periods. A vital input to the ERM is each hour forecasting demand, which can be done using correct forecasting methods. VPP can aggregate the all types of energy resources like, DGs, PV, electric vehicles, energy storage, demand response and electricity market. The use of Vehicle to Grid (or G2V), PV, and energy storage technology can help to increase the penetration of non dispatchable uncertain renewable based DGs. The drawback of large DERs penetration is that the optimal scheduling problem turns into a complex optimization problem and becomes hard to be addressed by deterministic techniques, because these techniques can take a large execution time for obtaining the optimal solution. On the other hand, the VPP has its own optimal scheduling related time constraints. For these reasons, metaheuristic techniques are very useful to support the VPP in the computation of a good solution with a low execution time. This paper proposed the new metaheuristic algorithm called Cross-Entropy Variable Neighborhood Differential Evolutionary Particle Swarm Optimization (CE-VNDEPSO) for addressing the Energy Resource Management (ERM) problem of 25-bus microgrid systems. The effectiveness of CE-VNDEPSO algorithm is finding out by comparing its performance with the well-known optimization algorithms like, Variable Neighborhood Search (VNS), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Particle Swarm Optimization (PSO) and Differential Evolution (DE).

In the MicroGrid environment, the high penetration of uncertain energy sources such as solar Photovoltaics (PVs), Energy Storage Systems (ESSs), Demand Response (DR) programs, Vehicles to Grid (V2G or G2V) and Electricity Markets make the Energy Resource Management (ERM) problem highly complex. All such complexities should be addressed while maximizing income and minimizing the total operating costs of aggregators that accumulate all types of available energy resources from the MicroGrid system. Due to the presence of mixed-integer, discrete variables and non-linear network constraints, it is sometimes very difficult to solve such problem using traditional optimization methods. This paper proposes a new metaheuristic optimization technique entitled the "Enhanced Velocity Differential Evolutionary Particle Swarm Optimization'' (EVDEPSO) algorithm to tackle the ERM problem. Its key feature is the updation of the Velocity by the terms named as Enhanced Velocity, Cooperation and Stochastic Uni-Random Distribution and position by the term Deceleration Factor. The performance of the proposed method is measured by a case study comprises of 100 scenarios of a 25-bus MicroGrid with high penetration of aforementioned energy sources. IEEE Computational Intelligence Society organized the competition on the above mentioned problem, in which EVDEPSO secured a second rank. The results of EVDEPSO are compared with the competition participated optimization algorithms. It also compared with well-known optimization algorithms such as Variable Neighborhood Search and Differential Evolutionary Particle Swarm Optimization. The comparison results show that the performance of EVDEPSO is superior in terms of the Ranking Index (R.I) and Average Ranking Index (A.R.I) as compared to the aforementioned algorithms. For effective comparative analysis of algorithms, standard statistical test named as One-Way ANOVA and Tukey Test is used. The results of this test also prove the effectiveness of EVDEPSO algorithm as compared to all tested algorithms.

Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.