Microgrid Reconfiguration Research Articles

Performance Enhancement for Microgrids Under the Demand Uncertainties with the Presence of Multiple DGs Through Stochastic Ranking Algorithm

The integration of Distributed Generators (DGs) in the reconfigurable microgrid is evolved to enhance the power delivery performance. The DGs are categorized as controllable and uncontrollable based on their power generation control behaviors. In addition to the control behavior, under the uncertain conditions of the microgrid, the performance of DGs is limited due to their respective drawbacks. Therefore, the identification of the most suitable DG under uncertain situation becomes more critical. This paper investigates the performance behavior of the hybrid DG, which uses the benefits of two kinds of DGs and overcomes their limitations. This work addresses five objective functions namely minimization of power loss, total generation cost, total emission cost and voltage deviation, and the maximization of the percentage DG penetration. The performance investigation of DGs is carried out for the system under five different test cases, including the uncertainty in power supply and demand. The modeling of the demand variation is done with the support of Probability Density Function (PDF). The many objective multi-indicator Stochastic Ranking Algorithm (SRA) is used for the optimization purposes. The simulations are performed on IEEE 33-bus Radial Distribution System (RDS) to assess the capability of the proposed investigation. Furthermore, the proposed strategy is applied under 24-h timely varying load pattern of IEEE 33-bus RDS to verify its applicability in uncertain load conditions. Moreover, the performance enhancement realization with the presence of hybrid DG is observed from the obtained simulation results.

Multi‐objective long‐term reconfiguration of autonomous microgrids through controlled mutation differential evolution algorithm

This study provides a solution for the feeder reconfiguration of autonomous microgrids (MGs). The objective is to minimise power loss, switching costs, and enhance voltage stability index, considering time-variations of loads. The daily load profiles for different seasons (spring, summer, fall, and winter) of different customers (i.e. residential, industrial, and commercial) are considered. In order to reduce the dimensions of the optimisation problem, k-means algorithm is implemented that clusters seasonal/yearly load profile into a few groups. The daily load profile is obtained based on the average of the group which has maximum members. This ensures the selection of a subset of load profiles that effectively represent the entire year's profile to reduce the complexity and execution time of the model. Subsequently, a new method is developed to break the daily load profile into intervals that guarantee less switching frequency for dynamic network reconfiguration. A controlled mutation differential evolution algorithm (CMDEA) compatible with long-term reconfiguration problem is developed with superior performance compared to conventional DEA and invasive weed optimisation algorithm. The CMDEA is employed to solve the reconfiguration problem on 33-bus and 69-bus autonomous MGs. Simulation results validate the effectiveness of the proposed method to reduce operational costs and computational burden in a smart grid environment.

Optimal Chance-Constrained Scheduling of Reconfigurable Microgrids Considering Islanding Operation Constraints

Microgrid concept is one of the suitable strategies for increasing resilience and preventing load curtailment, especially in emergency conditions. Operation in islanded mode is one of the unique features of microgrids that can provide numerous benefits for both consumers and energy producers. Unlike the conventional distribution networks, reconfigurable microgrids enable the reconfiguration process to achieve optimal structure. In this article, a new optimal strategy for scheduling of reconfigurable microgrids considering islanding capability constraints is presented. To demonstrate the successful islanding operation, the islanding capability is considered as a probability of islanding operation (PIO) index which shows the probability, that the microgrid has adequate level of spinning reserve to meet the local load. Taking into account the forecast errors of generated power by renewable energy resources (PV and wind) as well as load demand, the 13-interval approximation is used for the simplification of nonlinearity of PIO. The scheduling of reconfigurable microgrid with islanding operation constraints is formulated as a chance-constrained goal optimization problem, where the objective is defined as minimizing the total operation cost of microgrid in terms of fuel cost, reliability cost, cost of purchasing power from the mains, and switching cost. The proposed method is implemented on a 10-bus radial reconfigurable microgrid test system with photovoltaic (PV) panels, wind turbines, battery, and microturbines, with different levels of PIO. The numerical results show the effectiveness of the proposed scheduling method.

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model.

A techno-economic analysis: Urban reconfigurable microgrids participating in short-term regulating power markets

Power markets are undergoing structural alterations in accordance with emerging technologies. Traditional market rules and participants' behaviour are currently dominated by conventional power plants and synchronous generators. Renewable energy sources have gradually found their market share in the generation party specifically in developed countries. Apart from new energy resources, Demand Response Programs (DRP) have garnered attention due to substantial capabilities and potentials. This paper particularly sheds light on two flexible loads of Plugin Electric Vehicles (PEV) and cooling/heating responsive loads. PEV parking lots can be modelled as stochastic battery storages partially available for power exchange over short intervals. Besides, cooling/heating systems are naturally shiftable loads in short time horizons without deteriorating the resident's convenience. This study provides a comparative view of short-term power regulating markets in terms of bidding constraints and payment mechanisms. Consequently, a typical park & ride is considered as a reconfigurable urban microgrid to participate in short term regulating markets under different constraints. The results confirm that the internal coordination between the sub-units of microgrid brings more flexibility to enhance the total profitability. In addition, it is shown that the bidding constraints, as well as time slots, can be influential particularly in intermittent situations.

Privacy-Preserving Distributed Control Strategy for Optimal Economic Operation in Islanded Reconfigurable Microgrids

In this article, a privacy-preserving distributed control strategy is proposed for realizing the optimal economic operation of islanded reconfigurable microgrids (MGs). Using the proposed distributed control strategy, participating DERs would only exchange the frequency data with their neighbors while the generation data are held privately by each participant. The proposed distributed control strategy reduces the communication burdens among DERs and exploits the operational flexibility of reconfigurable MGs. It also demonstrates the versatility for considering a variety of operational objectives without requiring any additional communication and control infrastructures. Using the proposed control strategy, the optimality and the stability of an MG system's equilibrium point are demonstrated by the Lyapunov theory. The effectiveness of the proposed control strategy is validated in a 12-bus MG system for various operating conditions, including load variations, DER disconnection and reconnection operations, and MG reconfiguration operations.

Stochastic Modeling and Integration of Plug-In Hybrid Electric Vehicles in Reconfigurable Microgrids With Deep Learning-Based Forecasting

This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs' uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.

A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage

Due to the penetration of renewable energy sources (RESs) with probabilistic natures into microgrids (MGs), optimal scheduling and reconfiguration (RG) processes are associated with uncertainty. This paper presents the stochastic profit-based optimal day-ahead scheduling of a reconfigurable microgrid (RMG) as a new generation of the conventional microgrid. The proposed algorithm finds the optimal RMG's topology from the profit maximization point of view, the optimal hourly MG's unit set-points like micro-turbines (MTs) and energy storage, and power exchange with the main grid, simultaneously. The generated power of wind turbine (WT) and PV panel, as well as load demand are considered as uncertain parameters. To solve the profit maximization problem of RMG, time-varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is employed. Also, to ensure simulation accuracy in the presence of high-level uncertainties, the autocorrelation model is used based on actual data for the uncertainty of renewable power output. The feasibility and applicability of the proposed framework are demonstrated on a 69-bus radial RMG with various distributed generators in different cases. The results show the effectiveness of the proposed model.

Effect of P-Q Limits on Microgrid Reconfiguration: A Capability Curve Perspective

One of the proven approaches to achieve optimal operation in a microgrid is through network reconfiguration. The work presented in this article aims to carry out optimal reconfiguration on an islanded system. Optimal sizing and siting is generally done with an assumption of full availability of the apparent power of the distributed generator. However, the power available in a generator or inverter (for renewable energy sources) is lower than the apparent power. In this study, microgrid reconfiguration is revisited from the capability curve perspective. The limit imposed by four different curves for an inverter and a modified D curve for synchronous generator is used. Effect of reconfiguration on the system loss, radiality, voltage profile, reactive power reserves is investigated on a modified IEEE 33 bus system. It is observed that the system is found to have better voltage profile and less losses by incorporating reconfiguration feature using the capability curves.

Fuzzy Adaptive Granulation Multi-Objective Multi-microgrid Energy Management

This paper develops an energy management approach for a multi-microgrid (MMG) taking into account multiple objectives involving plug-in electric vehicle (PEV), photovoltaic (PV) power, and a distribution static compensator (DSTATCOM) to improve power provision sharing. In the proposed approach, there is a pool of fuzzy microgrids granules that they compete with each other to prolong their lives while monitored and evaluated by the specific fuzzy sets. In addition, based on the hourly reconfiguration of microgrids (MGs), granules learn to dispatch cost-effective resources. To promote interactive service, a well-defined, multi-objective approach is derived from fuzzy granulation analysis to improve power quality in MMGs. A combination of the meta-heuristic approach of genetic algorithm (GA) and particle swarm optimization (PSO) eliminates the computational difficulty of the nonlinearity and uncertainty analysis of the system and improves the precision of the results. The proposed approach is successfully applied to a 69-bus MMG test with results reported in terms of stored energy improvement, daily voltage profile improvement, MMG operations, and cost reduction.

A Risk-Averse Conic Model for Networked Microgrids Planning With Reconfiguration and Reorganizations

The advanced switching techniques enable the topology reconfiguration of microgrids (MGs) in active distribution network. In this paper, we enhance and generalize the traditional reconfiguration strategy resorting to the concept of “dynamic MGs” (i.e., the reorganization of MGs boundaries), to achieve a higher operational feasibility against the emergency islandings. Also, a risk-averse two-stage mixed integer conic program model is presented to support the networked MGs planning with generalized reconfiguration decisions. The MGs capacity expansion and seasonal reconfiguration decisions are made in the first stage, and validated under stochastic islanding scenarios in the second stage, where the network operations are captured by a second-order conic program (SOCP). Furthermore, a conditional value-at-risk (CVaR) measure is involved to quantitatively control the islanding risks. By theoretically proving the strong duality of the SOCP subproblem, we develop and customize Benders decomposition method with the guaranteed finite convergence to the optimal value. Finally, numerical results on 33- and 56-bus networked MGs validate the effectiveness of proposed reconfiguration strategy as well as planning approach. Our method demonstrates a cost-saving up to 22.56% when comparing to the traditional scheme with fixed MGs boundaries.

Enhancement of Distribution System Restoration Capability in Single/Multiple Faults by Using Microgrids as a Resiliency Resource

Natural disasters such as hurricanes and major floods result in extensive interruptions and lack of service to end-use customers in electrical distribution systems. Robust systems immune to extreme abnormal events are uneconomical and hard to achieve, so, fast and reliable solutions to restore the service after major outages are preferable. Rerouting the supply to critical loads and deploying microgrids are common solutions. In this paper, a sophisticated solution based on a combination of reconfiguration and application of microgrids is proposed to enhance the restoration capability of the distribution system using spanning tree search strategy. The coordinated plan would maximally restore the critical loads by using microgrids as emergency sources even when utility power is unavailable. The objectives are minimizing the number of switching operations, total system losses and out-of-service loads. A heuristic approach is proposed to simplify the graph of the distribution network in order to reduce the computational complexity. Instead of removing all degree one/two vertices, a threshold for simplification is applied to avoid establishment of zones with high loads that could not be restored by microgrids. The proposed method is simulated on a four-feeder, 1069-bus unbalanced test system with four microgrids to demonstrate its effectiveness.

Graph Theory and Optimization Algorithms Aided Adaptive Protection in Reconfigurable Microgrid

Microgrids interfaced with distributed generators facilitate decentralization of electric power. Bi-directional power flow due to multiple sources and dynamic behaviour of microgrids possess challenges to protection engineers. In this paper, an adaptive protection scheme for a central protection centre (CPC) in a microgrid is proposed. The key functions of the CPC are monitoring the microgrid, identification of fault if any, shortest path identification from a fault to the nearest operating source using Boruvka–Dijkstra graph theory algorithm. It also assigns adaptively the optimized values of time multiplier setting of relays in that path using genetic algorithm, which in turn aids in quick fault clearance. A hardware prototype is developed, tested and validated for a 7-bus microgrid network using Arduino ATmega 1280 for shortest path identification and optimized TMS value assignment for relays in that path using Raspberry Pi Model B+.

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating

This paper addresses the optimal operation and scheduling of reconfigurable microgrids incorporating the dynamic line rating limitations during the islanded and grid-connected mode operations. The incorporation of the dynamic line rating of overhead feeders can potentially improve the system security when providing economical and technical benefits for the microgrid. The proposed framework takes into account a realistic formulation to minimize the total microgrid costs in both grid-connected and multiperiod islanded modes. Also, a stochastic framework based on unscented transform is proposed to model the uncertainties associated with renewable energy sources output power, market energy price, and load demand, as well as the weather uncertain parameters such as solar radiation, wind speed, and ambient temperature. Due to the high nonlinearity and complexity of the proposed problem, an efficient optimization algorithm based on the collective decision optimization algorithm is proposed. A new two-stage modification method is also developed to improve the algorithm search ability and avoid premature convergence. The proposed problem is examined on the IEEE 32-bus microgrid test system. The simulation results show the effectiveness of the proposed model and verify its economic and reliability merits.

System reconfiguration in microgrids

Microgrids are emerging as an important technology in the smart grid era. They are particularly important from the perspective of integrating new renewable generation into the distribution side as well as ensuring system reliability. To operate the microgrid in an optimal and efficient way, one of the suggested technologies is the reconfiguration of the microgrid, which uses sectionalizing and tie switches already present in the system. This paper reviews the work done in microgrid reconfiguration so far, categorizing it on the basis of the several objective functions used, constraints considered, optimization techniques used, and methods of incorporating uncertainty inherent in renewable generation. The paper seeks to serve researchers and practitioning engineers with the state-of-the-art in the area and the research gaps and future challenges involved in microgrid reconfiguration are also highlighted.

Effective Dynamic Scheduling of Reconfigurable Microgrids

This paper develops an effective model for microgrid optimal scheduling with dynamic network reconfiguration. Network reconfiguration can effectively alter local power flow and thus provide an opportunity to reduce microgrid distribution network losses during grid-connected operation (supporting microgrid economic objectives) and to reduce potential load curtailments during the islanded operation (supporting microgrid reliability objectives). The proposed optimal scheduling model is decomposed into a grid-connected operation master problem and an islanded operation subproblem. A novel and highly accurate dynamic linear power flow model, with the ability of line switching, is developed and included in both problems. The optimal schedule determined in the master problem is assessed to meet the microgrid islanding feasibility in the subproblem. If infeasible, the decision variables are amended using the islanding cuts, which will accordingly revise the network reconfiguration, as well as the schedule of dispatchable units, energy storage, and adjustable loads. The simulation results on a test microgrid demonstrate the effectiveness and satisfying performance of the proposed model.

Optimal topology reconfiguration of microgrids considering electric vehicles

Optimal topology reconfiguration of microgrids considering electric vehicles

Optimal switching in reconfigurable microgrids considering electric vehicles and renewable energy sources

In this article, a smart stochastic framework is developed to use optimal switching to support increasing the role of renewable energy sources and electric vehicles in microgrid operation. The proposed method uses the point estimate method to model the uncertainties of electric vehicle charge and discharge demand, wind turbine, photovoltaics, load demand, and market price. The problem formulation is constructed in the form of a constrained single-objective optimization problem minimizing the total microgrid cost when meeting the practical operation constraints. Due to the high nonlinearity and the multi-modal characteristics of the proposed problem, a new optimization method based on the cuckoo search algorithm is proposed to solve the problem optimally. Also, a new three-stage modification approach is developed to increase the algorithm population and avoid the premature convergence. The feasibility and appropriate performance of the proposed method are examined on an IEEE test system. It is shown in the simulation results that using electric vehicle and optimal switching will result in better power dispatch amongst the distributed generations in the microgrid with lower power losses and costs.