A Linear-based Model for Multi-Microgrid Energy Sharing- A Western Australia Case Study

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. Specifically, MGs with surplus power can trade with those with deficit power. This paper presents a two-level hierarchical structure to manage the energy trading among MGs and also with the main grid. In lower level, individual MGs would operate as self-controlled entities; in upper level, an independent and nonprofit third party would coordinate the energy transaction among different MGs. To guarantee the fairness and autonomy, a local energy exchange market is established, in the presence of the main grid. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs’ bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power originally hidden in MGs. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.

Plummeting costs of Renewable Energy Sources (RES), Energy Storage Systems (ESS) and technological advancements in ICT has resulted in the mushroom growth of Microgrids (MG) around the world. Due to the high composition of variable, inflexible and uncertain RESs in MGs, at times of low RES output, MGs have to either unwillingly reduce consumption, turn on polluting conventional generators or purchase expensive electricity from the main grid. Conversely, at times of high RES output, there are instances where RESs have to be forcefully curtailed to maintain the stability of the network. In this paper, we introduce a novel Transactive Energy (TE) trading approach to reduce the effect of the inconvenient characteristics of RES generation in MGs. This is achieved by optimized sharing of resources across MGs through an aggregator agent, thus, further reducing the dependency on the main grid. Since resources are shared across MGs, blockchain as a technology tool is considered to accurately record transactions and remunerate participating assets.

Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system.

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.

With the growing need for energy, the concept of Microgrids (MG) with notable benefits for both production and demand sides was introduced. An MG consists of distributed generation units (DG), Energy Storage Systems (ESSs), and loads. It can operate either connected to the grid or in islanded mode. In the case of a fault or a disturbance, MG can be isolated from the main grid. Then, due to the imbalance between production and consumption, the MG frequency will deviate from the standard value. This makes the MG frequency will deviate from the standard value. The most important issue in the transition from connected to disconnected mode is to fix the disconnected MG frequency. This frequency deviation and its attenuation rate are of high importance for the demand side. To improve MG performance, MGs can be connected through a back-to-back (BTB) converter and form a multi-MG (MMG). Local DC resources are connected to their MG through an inverter, while other AC generations are directly connected to the MG. This article investigates the effects of MMG operation on each MG performance. It also examines the role of each MG in maintaining the balance between production and consumption in the MMG structure. It is assumed that each MG has at least one Micro-Turbine (MT) containing excitation control for its voltage regulation. Simulations are carried out in MATLAB Simulink environment to study frequency fluctuations. It is shown that the cooperation of the MGs can improve the performance of the MMG system.

This paper studies the real-time energy trading problem in a smart community consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. We propose a peer-to-peer (P2P) energy trading system, which integrates energy trading with energy management, enabling each prosumer to jointly manage its energy consumption, storage scheduling and energy trading in a dynamic manner for smart communities consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. The proposed community-based P2P energy trading system combines an online energy control and trading algorithm with a double auction mechanism. The energy control and trading algorithm is designed based on the Lyapunov theory, allowing each prosumer to independently determine its bid in each time slot only based on its current energy supply condition, while the trading price, which is determined via the double auction mechanism, reflects the collective energy supply conditions of all prosumers participating in energy trading. The integration of the Lyapunov-based energy control and trading algorithm and the double auction mechanism yields a dynamic energy trading pricing mechanism that induces the prosumers to participate in energy trading in a coordinated manner by influencing the energy consumption, energy charging/discharging and energy trading decisions of the prosumers. Numerical simulation results demonstrate that energy exchange in the proposed scalable energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while ensuring the fair sharing of the benefits reaped from energy trading among the prosumers.

In this paper, we present a Value-Decomposition Deep Deterministic Policy Gradients (V3DPG) based Reinforcement Learning (RL) method for energy trading and management of regional interconnected microgrids (MGs). In practice, the state of an MG is time-varying and the traded energy flows continuously, which is generally neglected in researches. To address this problem, an Actor-Critic framework is adopted. Each MG has to make energy trading decision based on local observation and has no access to any knowledge of other MGs. We bring in the idea of value-decomposition in the training process to ensure the generation of feasible cooperative policies while maintaining MGs' privacy and autonomous decision-making ability. Furthermore, in light of the uncertainty and fluctuation of renewable energy generation and users' demand, a recurrent neural network (RNN) with Burn-In initialization is combined with critic network to achieve implicit predictions. Meanwhile, we also take Energy Storage System (ESS) with operational constraints into consideration and deem it as a virtual market innovatively. Experiments have been carried out under real-world data to verify the merit of the proposed method, compared to existing RL-based works.

In recent years, most of the loads and distributed generations are connected to the AC grid through the power electronic converters. Using the DC grid beside the AC grid can reduce the conversion stages and power losses. Protection of the DC grids is a challenging issue because of the new structures of DC grids and fast transients of the DC faults. This paper studies the protection of the low voltage DC (LVDC) system in the presence of the photovoltaic (PV) and energy storage systems (ESS). An LVDC system consisting of a DC microgrid is considered and Different operating modes are analyzed. DC faults behavior and protection challenges are discussed for each mode through simulations employing MATLAB software. Finally, some methods are presented to solve the protection challenges. The results show that changing the protection arrangement of the system and choosing suitable control logics for the ESS and the PV prevent the unwanted outage of the loads and provide the possibility of the microgrid operation in islanded mode.

During the past few years, microgrids (MGs) have been becoming more attractive as effective means to integrate different distributed energy resources (DERs). To coordinate active and reactive power sharing among DERs, conventional droop control method is widely used as a decentralized control scheme. However, sharing powers among the sources based on the units' rated capacities is not an optimal solution in terms of economy and efficiency. In this paper, a new adaptive droop-based control strategy is proposed for AC MGs to optimally share MG load between corresponding units. The mentioned control strategy is developed in two levels. The upper control level is a mixed-objective optimization algorithm that provides optimal set-points for power generations considering system's constraints and goals, while the lower control level is responsible for tracking the reference signals coming from the upper level. To demonstrate the effectiveness of the proposed control strategy under different operating scenarios, simulation results in a benchmark MG are also presented.

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

Renewable Energies play crucial role in modern distribution systems. An active distribution system consists of several Micro-Grids (MGs) each of which has kinds of Distributed Generations (DGs) and Demand Response (DR) resources. Hence MG optimal operation is bi-level optimization problem whose upper level seeks for micro-grid owner profit and the lower level corresponds to distribution companies (DC) profit. In such a system MGs can exchange power between each other as well as distribution company. Therefore, upper level problem determines DGs generation, curtailed load, power exchange with other MGs subject to load balance constraint. Also Distribution Company (DISCO) clears retail market. On the other hand, renewable energies uncertainty and variably makes the MGs operation more complicated. In this paper, renewable energy variability handled by shift able load management and their uncertainty has been managed by curtailable load. The developed model has been solved by Particle Swarm Optimization (PSO) for upper level and then with given population in each iteration, lower level problem has been solved completely by other PSO package. This process ends when upper level PSO converge. Numerical studies have been shown that renewable management by proposed model leads to more optimal solutions.

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

The increasing number of distributed energy resources, energy storage systems and controllable loads, summarized as decentralized actors (DAs), causes new challenges for grid operation and planning. At the same time, it leads to new capabilities of forming microgrids (MGs) with controllable generation, consumption and storage functionalities in public distribution systems. To utilize MGs for ensuring grid stability in critical situations, necessary communication technologies are cost intensive and have to meet high security and reliability requirements. Modular microgrids (MMGs), which are decoupled from the upstream grid by power electronic grid interconnectors, offer the chance to use grid frequency as cost efficient and reliable communication channel. This paper presents a novel approach to control MMGs by frequency variation. To demonstrate the potential of the concept, the MMG with its DAs is modeled in DIgSILENT PowerFactory and simulation results are depicted.

Nowadays, the problem of the proper charging of plug-in hybrid electric vehicles (PHEVs) has become increasingly difficult because there is high uncertainty on both the demand and supply sides of microgrid (MG) systems, which may reduce efficiency and increase the cost of MG systems. Available solutions to these challenges are the application of energy storage systems and energy exchange with the upstream utility grid (UUG). However, uncoordinated PHEV charging could have adverse effects on MGs, such as increasing the amounts of energy exchanged between the MGs and the UUG as well as decreasing the battery energy storage systems (BESSs) lifetime. This article proposes a smart charging scheme for PHEVs in ac MGs that can simultaneously minimize the energy drawn from the UUG to charge PHEVs and maximize the lifetime of BESSs based on a multiobjective optimization algorithm. For proving the validity of the proposed power management strategy (PMS), offline digital time-domain simulation studies are conducted based on the modified version of the IEEE 33-bus test system. The obtained results are compared with the results of other previously reported PMSs, which show that by increasing the output power of DER units (even when the penetration level of PHEVs increases), the proposed PMS can ensure proper charging of PHEVs while improving the lifetimes of BESSs and decreasing the energy drawn from the UUG.

Plug-in hybrid electric vehicle (PHEV) has become a significant component in the fundamental infrastructure of microgrids (MGs). It is therefore of crucial practical interest to coordinate PHEVs with the rest of the grid. Supply demand coordination is an essential way to attain high energy efficiency at the system level while satisfying the stability of the grid requirement. This problem is challenging due to the high uncertainty from both the demand and supply sides in the system. This paper proposes a PHEVs smart charging that can achieve optimal operation for MGs. The proposed power management strategy is designed to minimize the MG dependency on the main grid, and to charge PHEVs based on maximum employment of RERs. Two scenarios are considered: the first scenario is designed so that PHEVs are charged without optimization and, the second case where PHEVs is charged in a smart way with optimization in order to reduce MG dependency on the main grid. Modified IEEE 33-bus system is simulated by MATLAB to verify the performance of the proposed power management strategy. The off-line digital time-domain simulations and software verification show that energy exchange between MG and the main grid is reduced. Also, the RERs generation is increased in order to charge the PHEVs.