A Framework for Resilient Coordinated Planning of Distributed Energy Resources in a Multi‐Microgrid System

To minimise the number of load sheddings in a microgrid (MG) during autonomous operation, islanded neighbour MGs can be interconnected if they are on a self‐healing network and an extra generation capacity is available in the distributed energy resources (DER) of one of the MGs. In this way, the total load in the system of interconnected MGs can be shared by all the DERs within those MGs. However, for this purpose, carefully designed self‐healing and supply restoration control algorithm, protection systems and communication infrastructure are required at the network and MG levels. In this study, first, a hierarchical control structure is discussed for interconnecting the neighbour autonomous MGs where the introduced primary control level is the main focus of this study. Through the developed primary control level, this study demonstrates how the parallel DERs in the system of multiple interconnected autonomous MGs can properly share the load of the system. This controller is designed such that the converter‐interfaced DERs operate in a voltage‐controlled mode following a decentralised power sharing algorithm based on droop control. DER converters are controlled based on a per‐phase technique instead of a conventional direct‐quadratic transformation technique. In addition, linear quadratic regulator‐based state feedback controllers, which are more stable than conventional proportional integrator controllers, are utilised to prevent instability and weak dynamic performances of the DERs when autonomous MGs are interconnected. The efficacy of the primary control level of the DERs in the system of multiple interconnected autonomous MGs is validated through the PSCAD/EMTDC simulations considering detailed dynamic models of DERs and converters.

Next Generation smart grid (SG) systems blend legacy power system networks and the latest state-of-the-art ICT technologies to ensure the efficiency, robustness as well as reliability of the former (power systems). The duplex flow of both information and energy enhances energy supply and de-mand response, as well as SG-related innovative business-oriented applications and services. Renewable generators (RGs) and Electric Vehicles (EVs) are becoming prominent in any SG setup as they promote environmental friendliness. The presence of both necessitates the optimal allocation of dis-tributed renewable generation (DRG) and energy storage sys-tems (ESS) at both SG and microgrid (MG) levels. In that way, grid stability will be always ensured. The paper describes and discusses an optimized ESS deployment approach for serving EVs (as they are one of the largest consumers of stored energy) and DRGs. Careful consideration of the state of the ESS is also considered by developing and applying a dynamic capacity adjustment algorithm to deal with the none-smooth cost func-tions. The proposed cost function takes into consideration the operation as well as investment cost minimization concurrently. The matrix real-coded genetic algorithm (MRCGA) is used to minimize the cost function of the system while constraining it to meet the customer demand, as well as the security of the system overall. The computational simulation results are presented to verify the effectiveness of the proposed method. The electricity network model is simplified using a virtual subnode concept to alleviate the computational load burden of a node's agent. Simulation results demonstrate the feasibility and stability of this dispatch strategy. Overall, our proposed framework and obtained results set a benchmark for the realization of agent-based coordination algorithms to solve the optimal dispatch problem

Since distributed energy resources (DERs) can provide capacity in distribution system, distribution network reinforcement interacts with DER planning (DERP) for meeting load growth. This article proposes the coordination of distribution network reinforcement and DER planning in competitive market. The model is formulated as a multi-stage programming. In first stage, distribution companies (DISCOs) and DER aggregators (DERAs) make bidding plans for network reinforcements and DERP. In second stage, DSO checks the network security. If the check fails, DSO sends capacity signals to DISCOs and DERAs for more capacities. In third stage, the distributed market is cleared by DSO’s optimal operation problem and distribution locational marginal price (DLMP) is returned to DERAs as price signal. An iterative algorithm is proposed to solve the multi-stage model. The algorithm embodies the process of information interaction among DISCOs, DERAs, and DSO, where DISCOs and DERAs compete in capacity market under DSO’s supervision. Numerical results on IEEE 33-system and an actual 141-bus system demonstrate that the proposed model coordinates distribution network reinforcement and DERP in competitive market.

Distributed energy resources (DERs) have the potential for the non-lines alternative in both transmission and distribution (T&D) systems. However, the value of DERs in substituting power lines in T&D systems is not revealed and coordinated planning of T&D networks and DERs under market environment is not studied in existing researches. This may damage the interests of DER investors, hinder the wide integration of DERs in power system and increase total cost under market environment. This paper proposes market-based coordination of transmission and distribution network planning (TDNP) with DER planning (DERP). The model is formulated as multi-stage programming. In first stage, transmission companies (TRANSCOs), distribution companies (DISCOs), and DER aggregators (DERAs) send their bidding plans to independent system operator (ISO) and distribution system operators (DSOs), respectively. In second stage, ISO and DSOs check the transmission and distribution network securities, respectively. In third stage, ISO and DSOs solve their optimal operation problems, respectively. An iterative solution algorithm is proposed to solve the multistage model. The solution embodies the interaction process among TRANSCOs, DISCOs, DERAs, ISO, and DSOs in electricity market. Numerical results demonstrate that the proposed model coordinates TDNP with DERP, promotes DER installations and reduces the total cost.

This study proposes a double-stage double-layer optimization model for a virtual power plant (VPP) consisting of interconnected microgrids (IMGs) with integrated renewable energy sources (RESs) and energy storage systems (ESSs) to realize demand-side ancillary service, considering intra energy sharing among the IMGs within the VPP. In particular, the first stage, day-ahead scheduling, is carried out to predict the hourly electricity consumption baseline and regulation capacity for the next day, the latter of which results in a reward from the market operator. In the second stage, real-time power consumption control is performed by following the dynamic regulation (or RegD) signal. The second stage is further divided into two layers: the upper layer distributes demand response (DR) signals from the main grid according to the electricity unit price of each microgrid (MG) and exchanges electricity among MGs based on a new energy sharing mechanism to reduce RegD-following violations. The lower layer performs real-time power consumption control for each MG to minimize operation costs. The overall goal is to maximize the reward in the day-ahead stage and minimize the RegD-following violation penalty in the real-time stage, so as to minimize the overall operation cost of the VPP. The optimization is written in five objective functions, which are solved using mixed integer linear programming (MILP) in Gurobisolvers. Extensive simulation and comparison studies are carried out, and numerical results show that compared with traditional MG operations, VPPs comprised of IMGs can reduce operation costs and provide better frequency support for the grid through superior RegD signal following performances.

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.

This paper aims at co-optimizing day-ahead operation schedules of distributed energy resources (DER) in a coordinated microgrids (MG) cluster to enhance resiliency. The proposed model strategically integrates the potential flexibility provided by the DERs in neighboring MGs, while capturing the joint portfolio flexibilities on an hourly basis scheduling scheme. In this context, the proposed optimization model, which is formulated as a mixed-integer linear programming (MILP) problem, minimizes the total operation cost of the MGs in both normal and emergency cases, where the upstream grid might be unavailable in the latter leading the MGs to work in an autonomous mode. In order to reveal the merits of the proposed model, multiple case studies are investigated through the modified IEEE 16-node test feeder, where we decompose the original system to a 6- and 10-node systems denoted by MG 1 and MG 2, respectively.

The microgrids (MGs) are considered to be a key component of future power systems. The increased integration of distributed energy resources (DER) has led to critical challenges in energy management (EM) such as uncertainty, pricing and optimal dispatch. The need for control of DER, optimal resource allocation and EM, requires interconnection of MGs, leading to formation of multiple-microgrid (MMG). One of the fundamental objective of MMG is to make optimal use of DER's within various MGs for optimal benefits. At a particular instance, some MGs might have excess energy generated, while some other MGs might buy the excess energy to meet local demands and storage requirements. The MMG facilitates energy trading between the grid and MG and amongst the MGs in order to achieve efficient energy sharing. In this paper, we evolve through different EM and scheduling strategies using visual basic for applications (VBA). The system considered has 3 MGs interconnected forming a MMG with each MG equipped with PV system and energy storage system (ESS). The objective of the strategies is to maximize the profits of individual MG while benefiting the whole MMG in total. The costs of implementing the proposed strategies are calculated and an economic analysis of various strategies is presented.

Interconnection of microgrids (MGs) provides the opportunity of increasing loadability and enhancing reliability by allowing power exchange among individual MGs. Control of networked MGs requires coordination of distributed energy resources (DERs) within each MG such that the sum of the MG's load and exported power is shared proportionally among DERs while maintaining power quality. This paper proposes a new hierarchical control scheme for accurate load sharing and power quality enhancement in networked MGs with unbalanced load currents. The proposed scheme is comprised of local DER controllers, MG control units, and Networked MG control center. The MG control units adjust the power exchange at the value specified by the control center and regulate the voltage at the point of common coupling (PCC) by means of a secondary control signal. At the DER level, droop control is adopted to enable load sharing with fast dynamic response. The droop controller output is combined with the secondary control signal and a correction voltage which eliminates the effect of line impedances on the current sharing accuracy. This way, accurate sharing of individual phase currents is realized without requirement of data exchange among DERs. Simulation and experimental results showcase the effectiveness of the proposed scheme.

Remote area microgrids (MG) can experience overloading or power deficiency throughout their dynamic operations due to load and generation uncertainties. Under such conditions, load-shedding is traditionally considered as the first successful mechanism to prevent system instability. To minimize load-shedding, islanded neighboring MGs can be connected to each other in remote areas to provide a self-healing capability. For this, extra generation capacity needs to be available in the distributed energy resources (DER) of one of the MGs to supply the extra demand in the other MG. In this way, the total load in the system of interconnected MGs will be shared by all the DERs within those MGs. This process falls within the network tertiary controller functions. Therefore, the tertiary controller should have a self-healing algorithm that needs to be carefully designed to initiate the command for interconnection of the MGs. The self-healing strategy needs to consider the required criteria to prevent system instability. The MGs will then be interconnected through an interconnecting static switch (ISS). This strategy also needs to decide when two interconnected MGs should be isolated. This paper focuses on the self-healing strategy, its criteria and features. The efficacy of the developed strategy in interconnecting and isolating the neighboring MGs is validated through PSCAD/EMTDC simulations.

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the annualized cost of community MG, including investment cost in DER, operation cost for dispatchable fuel-generators (DFG), energy storage system (ESS) degradation cost, energy purchasing and peak demand charge at PCC, customer discomfort cost due to the room temperature deviation and load curtailment cost. Given the slow thermal dynamic characteristics of buildings, the heating, ventilation and air-conditioning (HVAC) system in the proposed model is treated as a demand side management (DSM) component, whose dispatch commands are provided by the central MG controller. Numerical results based on a community MG comprising of 20 residential buildings demonstrate the effectiveness of the proposed planning model and the benefits of introducing the building thermal dynamic model.

As the needs for distributed renewable generation (DRG) increase driven by the accelerated growth of electric demand in order to deter greenhouse emissions and create a more economic and efficient power supply environment, this papers pays attention to the potential contribution of independent owners (IOs) of DRG operating within the microgrid (MG). In order to assess such role, an optimization scheme is introduced to allocate and size the stochastic DRG with a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for power distribution loss, dynamic pricing environment, demand response, stochastic generation, etc. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that gradually schedule the resources maximizing the objective function and coping with the complicated nonlinear nature of the problem and enabling of efficiently carrying long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) working on evolutionary basis to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan is used for testing the algorithm and analyzing the potential of IO investments and their strategies.

Technological development and restructuring of power system have encouraged the Independent Power Producers (IPPs) to connect their Distributed Energy Resources (DERs) to the existing power system. DERs can be used to its maximum efficiency through MicroGrid (MG). This also led to a path for Smart MicroGrid (SMG) where DERs are scheduled and dispatched economically as well as eco-friendly. Due to intermittent nature of Renewable Energy Sources (RESs) in MG and dynamic nature of demand static dispatch of modern power system is not of any use. Thus, it is very important to research economical scheduling and associated dispatch of power system connected with MG. This considers random variations of DERs referred as dynamic dispatch. This paper presents the economic as well as environmental dispatch scheduling of a power system connected to MG composed of different distributed energy resources and storage devices. The overall generation cost of static and dynamic dispatch of proposed system has also been compared. A mathematical model of given system has been formulated considering all the system constraints and ramp rate constraint of generators to minimize the operational and pollutant treatment cost. The optimization study has been carried out in two different modes: (i) Grid connected with MG; and (ii) Isolated grid operation. Mixed Integer Non Linear Programming technique has been used to solve the Unit Commitment (UC) and dispatch problem. The influence of scheduling strategies on operational cost and pollutant treatment cost has been obtained and compared and the impact of MG interconnection with the power system has been shown in terms of operational cost, pollutant treatment cost, total cost and on the unit commitment of power system to reduce the burden on grid generators.

Power deficiency management is an important factor in the operation of remote microgrids (MG). Load-shedding is traditionally considered as the main mechanism to manage the network under power deficiency conditions. To minimize load-shedding, islanded neighboring MGs can be connected to each other in remote areas to provide a self-healing capability. For this, extra generation capacity needs to be available in the distributed energy resources (DER) of one of the MGs to supply the extra demand in the other MG. In this way, the total load in the system of interconnected MGs will be shared by all the DERs within those MGs. This paper presents a strategy which aims to interconnect two neighboring microgrids in remote areas to minimize the necessity of load-shedding. This strategy also needs to decide when two interconnected MGs should be isolated. This paper focuses on the self-healing strategy, its criteria and features. The presented algorithm in this paper does not need any data communication system for its operation. The performance of the developed technique is validated by PSCAD/EMTDC simulations.

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - points provided to MPC are forecast for future instances by a linear extrapolation to gain smooth active and reactive power exchange under various loading conditions (e.g. balanced / imbalanced, nonlinear and dynamic loading) and voltage imbalance imposed by the upstream grid. In this scheme active and reactive power control change to current control and the references of the currents are forecast. The stability of the proposed control scheme is analyzed and discussed. The effectiveness of the proposed scheme is demonstrated by extensive time - domain simulations using PSCAD / EMTDC for various conditions (various loads, voltage imbalance, parallel operation with other DGs, parameter uncertainties and measurement noises) in several case studies. Comparing the obtained results with those of the two other schemes (PI - based and convectional MPC) shows the superiority of the proposed scheme.