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Abstract In addition to providing mobility, plug‐in hybrid electric vehicles (PHEVs) provide a two‐sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all.

Abstract Automatic reconfiguration is one of the key actions in self‐healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance‐constrained programming‐based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks.

Recently, the number of Battery Energy Systems (BESs) connected to the grid has grown significantly. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with a linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability and decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in the proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with a day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.

Abstract Proliferation of distributed energy resources (DERs) calls for a coordinated transmission and distribution (T&D) scheduling at the day‐ahead stage. The problem becomes more complicated dealing with the variability of stochastic parameters. Also, privacy and complexity are two barriers to the development of such coordinated platforms. This paper addresses these issues by introducing a hybrid centrally‐supported decentralized stochastic framework for the day‐ahead energy and reserve market with minimum complexity and the need for data‐sharing between system operators. The proposed model is able to calculate the bidirectional power exchange at the T&D interface and the separated costs, dispatches, and reserves of all market participants. The proposed model does not consider any priority for operators and increases the liquidity by facilitating participants’ access to the market platform. Also, the second‐order cone programming (SOCP) formulation is used for calculating the AC power flow of distribution grids, and the model is validated and compared with other implementation strategies. The proposed model is implemented on a modified IEEE 24‐bus test system, and results show that the model can schedule resources for supplying energy and reserves in both transmission and distribution levels in an acceptable computation time.

Abstract The secondary control layer of microgrids is often modelled as a multi‐agent distributed system, coordinated based on consensus protocols. Convergence time of consensus algorithm significantly affects transient stability of microgrids, due to changes in communication topology, switching of distributed generations (DGs), and uncertainty of intermittent energy sources. To minimise convergence time in consensus protocol, this work proposes a multilayer event‐based consensus control framework, which is resilient to communication delays and supports plug‐and‐play (P&P) addition or removal of DGs in DC microgrids of cellular energy systems. A novel bi‐layer optimisation algorithm minimises convergence time by selecting an optimal communication topology graph and then adjusts controllers' parameters. Average consensus is achieved among distributed controllers using an event‐based consensus protocol, considering non‐uniform delays between agents. A realisation method has also been introduced using the directional beamforming technique for topology assignment algorithm based on modern telecommunication technologies. Provided feasibility case study has been implemented on a real‐time hardware‐in‐the‐loop (HIL) experimental testbed, to validate the performance of the proposed framework for key purposes of voltage stabilisation and balanced power‐sharing in DC microgrids.

Abstract The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called “resilience metrics”. Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the “area‐based” resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures.

Despite the fact that power grids have been planned and utilized using centralized networks for many years, there are now significant changes occurring as a result of the growing number of distributed energy resources, the development of energy storage systems and devices, and the increased use of electric vehicles. In light of this development, it is pertinent to ask what an efficient approach would be to the operation and management of future distribution grids consisting of millions of distributed and even mobile energy elements. Parallel to this evolution in power grids, there has been rapid growth in decentralized management technology due to the development of relevant technologies such as blockchain networks. Blockchain is an advanced technology that enables us to answer the question raised above. This paper introduces a decentralized blockchain network based on the Hyperledger Fabric framework. The proposed framework enables the formation of local energy markets of future citizen energy communities (CECs) through peer-to-peer transactions. In addition, it is designed to ensure adequate load supply and observe the network’s constraints while running an optimal operation point by consensus among all of the players in a CEC. An open-source tool in Python is used to verify the performance of the proposed framework and compare the results. Through its distributed and layered management structure, the proposed blockchain-based framework proves its superior flexibility and proper functioning. Moreover, the results show that the proposed model increases system performance, reduces costs, and reaches an operating point based on consensus among the microgrid elements.

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.

Over the last decades, environmental concerns and the global tendency to reduce the use of fossil fuels and replacing them with renewable energy sources (RESs) to face the increasing rate of greenhouse gas (GHG) emissions have increased. Buildings consume a significant amount of energy and therefore, they are responsible for a noticeable part of the total GHG emission. Thus, when we talk about decarbonization of the energy systems, buildings are an important sector of the energy system that needs to be considered. Using RESs, smart technologies, and information and communication technologies along with the improvement in energy efficiency, are a number of endeavors to increase the role of building on the way toward decarbonization. In the new environment, the buildings are not passive players of the energy systems and they are able to take an active role and participate in the energy-efficient operation. While they are able to manage their resources and serve the local energy requirements of the residents in the best possible manner, they can participate in the energy and balancing markets and support the network operators as a service provider. In this paper, we present a comprehensive review of active buildings’ concept, challenges and outlook to pave the way for the researchers from academia and industry who want to start working in this area.

Planning of the electric distribution networks is complex and about upgrading the system to satisfy the demand and constraints with the best economic plan. The planning alternatives include the expansion of substations, installing new distributed generation (DG) facilities, upgrading distribution feeders, etc. In the modern networks, distribution planners must gain the confidence of the reversibility of the investment where renewable energy resources (RERs) inject clean and cost-effective electrical power to respond to the rising demand and satisfy environmental standards. This paper is an exhaustive review on the distribution network expansion planning (DEP) including the modelling of DEP (possible objective functions, problem constraints, different horizon time, and problem variables), optimization model (single/multi-objective), the expansion of distributed energy resources (DERs), problem uncertainties, etc. We discuss the requirements of integrated energy district master planning to avoid conflicts between the goal of independence of district planning on energy, e.g. heat and electricity, and that of dependencies on the local electric utilities regarding instant power balance and stability services. Finally, we describe the primary future R&D trends in the field of distribution network planning.

This paper describes an overview of the optimal energy coordination/management approaches for microgrids. The article presents the smart grid environment in conjunction with their technologies into the applications of a microgrid when the energy coordination aims to create power flow stability between the generation and consumption of the electricity. This energy equilibrium is made regardless of a power grid complexity that can contain diverse load demands and distributed energy resources (DERs), including renewable energy system (RES), energy storage system (ESS), electric vehicle (EV), etc. A microgrid often contains an energy mix system that requires three control levels, namely primary, secondary and tertiary, to optimize the energy cost and behavior of the system operation and exploitation. Based on several DERs, a microgrid can operate in island mode or be connected to the main grid. The energy coordination for both features is to deal with the energy resources uncertainty, the climate impact, to reduce atmospheric pollution deriving from the conventional power grid, and the energy demand growth. Through the smart grid technology, the optimization approaches of this coordination have brought several improvements into the electrical system. Thus, an overview of an intelligent energy management system for microgrid applications is intensively detailed to structure the implementation strategies which aim to coordinate the energy flow of an electrical system optimally.

Demand-side response programs, commonly known as demand response (DR), are interesting ways to attract consumers’ participation to improve electric consumption patterns. Customers are encouraged to modify their usage patterns in reaction to price increases through DR programs. When wholesale market prices are high or network reliability is at risk, DR can help to establish a balance between electricity generation and consumption by providing incentives or considering penalties. The overall objective of adopting DR programs is to increase network reliability and decrease operational costs. Nevertheless, the successful deployment of DR programs requires a set of conditions without which no success can be guaranteed. Implementing DR programs and achieving customers’ optimal power consumption behavior could be obtained through technical methods, such as using smart home appliances and big data techniques. However, even if each of these approaches is correctly implemented, they are not able to address all aspects of the problem. The findings of several studies demonstrate that, in addition to technical and economic concerns, social, cultural, and behavioral variables play a significant role in DR implementation. Therefore, this paper investigated the social, cultural, and behavioral variables as critical requirements for implementing DR programs. Furthermore, a theoretical framework and an analytical model of the elements impacting the electricity consumption are introduced that should be considered by the planners.

The way the world gets its energy is undergoing a rapid transition, driven by both the increased urgency of decarbonizing energy systems and the plummeting costs of renewable energy technologies [...]

This paper presents a novel concept of "smart distribution system expansion planning (SDEP)" which expands the concept of demand response programs to be dealt with the long term horizon time. The proposed framework, integrates demand response resources (DRRs) as virtual distributed generation (VDG) resources into the distribution expansion planning. The main aim of this paper is to develop and initial test of the proposed model of SDEP to include DRRs which are one of the most important components to construct smart grid. SDEP is modeled mathematically as an optimization problem and solved using particle swarm optimization algorithm. The objective function of the optimization problem is to minimize the total cost of lines’ installation, maintenance, demand response persuasion, energy losses as well as reliability. Furthermore, the problem is subject to the constraints including radiality and connectivity of the distribution system, permissible voltage levels, the capacity of lines, and the maximum penetration level of demand response. Based on two sample test systems, the simulation results confirmed that the consideration of DRRs simultaneously with distribution system expansion can have economical profit for distribution planners.