Operation Of Networked Microgrids Research Articles

A decentralized framework for economic‐resilient operation of networked microgrids, considering demand response program

AbstractThe occurrence of natural disasters can disrupt the normal functioning of distribution networks. In this situation, the activeness of distribution networks in the sense of benefiting from energy sources and related structures such as microgrids (MGs) makes them benefit from the capability of structures such as MGs, in case of High‐Impact Low‐Probability (HILP) events, so that consumers are able to disconnect from main grid and trade with other MGs. Thus, the energy management of networked MGs needs a new operation structure to encourage them providing each other with the required energy by creating the necessary incentives. This paper presents a novel cooperative framework aimed at enhancing the resilience of MGs through the establishment of peer‐to‐peer (P2P) energy trading among their constituents. In the present model, MGs engage in energy transactions with one another, aiming to minimize costs through the cooperative operation of networked MGs. The utilization of the suggested model enables MGs to enhance network resilience within the framework of peer‐to‐peer energy transactions, while also achieving cost reductions in comparison to islanding and non‐cooperative operation. The numerical results obtained demonstrate the efficacy of the proposed methodology in enhancing the resilience of MGs and mitigating their operational expenses.

Adaptive Load Shedding as Part of Primary Frequency Response To Support Networked Microgrid Operations

Global changes in the deployment of distributed energy resources, control and communications technologies, business models, and regulatory policy are increasing the operational options for future distribution systems. One such option is the coordinated operation of distributed resources to form microgrids and networks of microgrids to support traditional bulk power systems during normal operations and critical end-use loads during outages. While individual standalone microgrids have been extensively studied and deployed, the coordinated operation of networked microgrids is operationally more challenging due to the dynamic boundaries and changing mix of generation resources. This paper presents a method of using a distributed control architecture to support primary frequency response in networked microgrids operations. The support of primary frequency response is accomplished using the Open Field Message Bus reference architecture and Grid Friendly Appliance controllers.

Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing.

Optimal sizing of RES and BESS in networked microgrids based on proportional peer‐to‐peer and peer‐to‐grid energy trading

AbstractNetworked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed “proportional trading method” via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid. In this paper, a set of smart railway stations, which is assumed as microgrids, is connected together. It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid. Also, the operational costs of stations under various conditions decrease by applying the proposed method. The smart railway stations are studied in the presence of photovoltaic (PV) units, energy storage systems (ESSs), and regenerative braking strategies. Studying regenerative braking is one of the essential contributions. Moreover, the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method. The networked microgrid scheme of railway stations (based on coordinated operation and scheduling) and independent operation of railway stations are studied. The proposed method is applied to realistic case studies, including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company (TUSROC). The rolling stock is simulated in the MATLAB environment. Thus, the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming (MILP).

Probabilistic optimization of planning and operation of networked microgrids with renewable energy resources considering demand response programs

Probabilistic optimization of planning and operation of networked microgrids with renewable energy resources considering demand response programs

A Three-Level Planning Model for Optimal Sizing of Networked Microgrids Considering a Trade-Off Between Resilience and Cost

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing.

Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles

The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.

Privacy-Preserving Collaborative Operation of Networked Microgrids With the Local Utility Grid Based on Enhanced Benders Decomposition

In this paper, a privacy-preserving decision-making framework is developed for a collaborative operation of networked microgrids and the local utility grid, where the distribution system operator (DSO) interacts with microgrid master controllers (MCs) in a leader-followers fashion. First, the paper models the decision-making in the collaborative operation presented as an integrated mixed-integer second-order cone programming problem which satisfies various technical and operational requirements in a look-ahead manner. Then, the integrated optimization problem is decomposed into a master problem and a set of subproblems in order to decentralize the decision-making process of DSO and MCs. The master problem is solved by DSO for optimizing the local utility grid operation, and the subproblems are solved independently by MCs for optimizing the operation of individual microgrids. We propose an enhanced Benders decomposition algorithm to guide the iterations between the master problem and subproblems. In particular, an enhanced approach is introduced in the Benders decomposition algorithm for generating valid cutting planes from mixed-integer linear programming subproblems. Mathematically, the proposed algorithm can find the globally optimal solution after a finite number of iterations. Accordingly, DSO and MCs manage to collaborate on making operational decisions that would maximize the social welfare without compromising independent decision-making and privacy provisions. Numerical experiments are conducted on a simplified two-bus system and a modified IEEE 123-bus system. The results validate the efficiency, the robustness, and the scalability of the proposed collaborative decision-making framework in power systems.

A Two-Layer EMS for Cooperative Sugarcane-based Microgrids

This paper presents a solution for the energy magement and resource sharing problem of cooperative renewable-energy-based microgrids. Such solution is based on a two-level hierarchical optimization procedure to coordinate the operation of networked microgrids and optimally share their combined energetic resources, aiming to enlarge the renewable generation share while guaranteeing maximal efficiency and profit.The supervision layer is a Real-Time Optimization scheme, designed to determine energy-generation set-points for the distributed plants as well as resource sharing rules, taking into account their models, operational constraints and future predicted behaviour of the renewable sources. The lower layer is a Model Predictive Control loop, designed to follow energy generation set-points passed top-down by the upper layer and abide by demands, while respecting operational constraints.This work is focused on cooperative microgrids based on Brazilian sugarcane industries. Such plants are usually located in various sites, while being owned by the same company that has a fixed energy contract with the Distribution Network Operator. The biomass sources may be transported from one plant to another, if deemed convenient. This case study is taken into account and high-fidelity numerical simulations are presented to assess the effectiveness and qualities of the proposed energy management strategy, which yields overall good results.

Optimal Consensus-Based Distributed Control Strategy for Coordinated Operation of Networked Microgrids

A two-layer optimal consensus-based distributed control strategy is proposed for the coordinated operation of networked microgrids (MGs). Several networked MG operation objectives are optimized using the proposed control strategy, including frequency/voltage regulation, proportional active power sharing, and smooth MG islanding/reconnection operation. The global objective function of networked MGs is decomposed into a series of local objectives assigned to participating distributed energy resources (DERs). In the decomposed optimization, each control layer is realized in a distributed manner in which DER state variables reach consensus along with optimizing their assigned local objective functions. Each control layer in the two-layer control strategy is formulated as an optimal consensus problem in which the optimality and asymptotical stability of the system equilibrium point for each control layer are demonstrated. The effectiveness of the proposed control strategy is validated in a modified IEEE 33-bus distribution system using the PSCAD/EMTDC platform.

Conservation voltage reduction of networked microgrids

This study proposes a strategy for coordinated operation of networked microgrids (MGs), distributed energy resources (DERs), and volt-VAR control devices in the implementation of volt-VAR optimisation (VVO). The authors’ formulation is focused on implementing conservation voltage reduction, although it can be extended for any other VVO problems. They consider the voltage–power control modes of DERs contemplated in IEEE Std. 1547, namely, constant power factor, voltage–reactive power, active–reactive power, and constant reactive power. DERs are considered at the distribution network (DN) and within the MGs. The problem is formulated as a bi-level optimisation problem where the DN operator (DNO) is in the upper-level and each MG in the lower-level. The DNO and each MG are assumed to be players with their individual objective functions. Finally, they validate their formulation in two test systems: a 9-node system and a modified IEEE 33-node system. On average the proposed method can improve the efficiency of the grid by 4% compared to the economic operation scenario. The results of the study show that the different control modes of the DERs can have better performance depending on the objective of the VVO formulation. The findings of this study are compelling for the implementation of VVO in active distribution systems with networked MGs.

A robust optimization approach for risk-averse energy transactions in networked microgrids

Abstract In order to alleviate the risk of energy transactions in networked microgrids (MGs) under uncertainties, such as renewable energy and transaction prices, a risk-averse energy transaction approach is proposed based on robust optimization. A systematic combinatorial optimization is applied to model energy exchanges among MGs, and then a two-stage robust optimization model for energy transactions is formulated to optimize the joint operation of networked MGs. Furthermore, the proposed model can be effectively solved by column-and-constraint generation (C&CG) algorithm. Under different budgets of price uncertainty, the results show the proposed approach can not only achieve the optimal operational cost of networked MGs, but also ensure the robustness of energy transactions among MGs.

Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables

Microgrids have the potential to withstand the power outages due to their ability of islanding and potential to sustain the penetration of renewables. Increased penetration of renewables can be beneficial but it may result in curtailment of renewables during peak generation intervals due to the limited availability of storage capacity while shedding loads during peak load intervals. This problem can be solved by adjusting the load profiles, i.e., demand response (DR) programs. In contrast to the existing studies, where DR is triggered by market price signals, a local resource-triggered survivability-oriented demand response program is proposed in this paper. The proposed DR program is triggered by renewable and load level of the microgrid with an objective to minimize the load shedding and curtailment of renewables. The uncertainties in load and renewables are realized via a robust optimization method and the worst-case scenario is considered. The performance of the proposed method is compared with two conventional operation cases, i.e., independent operation case and interconnected operation case without DR. In addition, the impact of renewable penetration level, amount of shiftable load, and load absorption capacity on the performance of the proposed method are also analyzed. Simulation results have proved the proposed method is capable of reducing load shedding, renewable curtailment, and operation cost of the network during emergencies.

Improving Primary Frequency Response to Support Networked Microgrid Operations

Individual microgrids have proven their ability to provide uninterrupted power to critical end-use loads during severe events. Building on the performance of individual microgrids during extreme events, there has been an increasing interest in the operations of network microgrids. By networking microgrids during extreme events, it is possible to share resources, increase the duration for which they can operate islanded, improve efficiency, and increase the resiliency of critical end-use loads. While there are benefits to networking the operations of resiliency-based microgrids, the switching operations that they require introduce transients, which can result in a loss of dynamic stability. The issue of dynamic stability is especially acute in microgrids with high penetrations of inverter-connected generation, and correspondingly low system inertia. While the low inertia of these microgrids can be increased by over-sizing the rotating generators, the increased capital and operating costs can become a barrier to deployment. This paper will present a method of augmenting primary frequency controls to support the switching transients necessary for the operation of networked microgrids, without the need to over-size rotating machines.

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Operation of networked microgrids in a distribution system

Operation of networked microgrids in a distribution system

Coordinated Energy Management of Networked Microgrids in Distribution Systems

This paper proposes a novel control strategy for coordinated operation of networked microgrids (MGs) in a distribution system. The distribution network operator (DNO) and each MG are considered as distinct entities with individual objectives to minimize the operation costs. It is assumed that both the dispatchable and nondispatchable distributed generators (DGs) exist in the networked MGs. In order to achieve the equilibrium among all entities and take into account the uncertainties of DG outputs, we formulate the problem as a stochastic bi-level problem with the DNO in the upper level and MGs in the lower level. Each level consists of two stages. The first stage is to determine base generation setpoints based on the load and nondispatchable DG output forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. A scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Case studies of a distribution system with multiple MGs of different types demonstrate the effectiveness of the proposed methodology. The centralized control, deterministic formulation, and stochastic formulation are also compared.