Microgrid Distribution Network Research Articles

A single and multiobjective robust optimization of a microgrid in distribution network considering uncertainty risk

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24 h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters.

Two-Stage Optimal Scheduling Strategy of Microgrid Distribution Network Considering Multi-Source Agricultural Load Aggregation

With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Smart transactive energy based approach for planning and scheduling in multi-looped microgrid distribution network across planning horizon

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach

This work presents a distributed robust operation framework for the microgrids within the power distribution network. It addresses the uncertainties in photovoltaic generation, as well as active, and reactive loads in the network, by modeling them using polyhedral sets. The energy management problems are formulated as two-stage optimization problems in microgrids and power distribution networks for which Benders decomposition is used to solve the problems. The power flow constraints are relaxed using second-order cone relaxation and moment relaxation techniques in the power distribution network and microgrid energy management problems, respectively. The interaction between the microgrids and the power distribution network is represented using the price signal and the exchanged active and reactive power at the microgrids’ points of common coupling. The effectiveness of the proposed framework is shown using two case studies on IEEE 37-bus and IEEE 123-bus systems. The effect of the energy storage system on the operational cost of the system is assessed. It is shown that total operation cost is decreased by 9.69% when the energy storage system is integrated into the power distribution network. Additionally, the solution derived from the robust problem is compared with that of the deterministic problem.

Improving Directional Overcurrent Relay Coordination in Distribution Networks for Optimal Operation Using Hybrid Genetic Algorithm with Sequential Quadratic Programming

In recent years, with the growing popularity of smart microgrids in distribution networks, the effective coordination of directional overcurrent relays (DOCRs) has presented a significant challenge for power system operators due to the intricate and nonlinear nature of their optimization model. Hence, this study proposes a hybrid GA-SQP algorithm to enhance the coordination of directional overcurrent relays (DOCRs) in radial and non-radial interconnected distributed power networks. The proposed approach combines the advantages of both the genetic algorithm (GA) and sequential quadratic programming (SQP) methods to optimize the objective function of relay coordination in the best manner. Thus, the proposed hybrid techniques improved the convergence of the problem and increased the likelihood of obtaining a globally optimal solution. Finally, to validate the effectiveness of the proposed algorithm, it was tested through three case studies involving the IEEE 3-Bus, 8-Bus, and modified 30-Bus distribution networks. In addition, the results were compared to those obtained using previous methods. The results obtained from the comparison of the proposed method and recent advanced research indicate that the proposed optimization approach is preeminent in terms of accuracy and total operating time as well as the continuity of the minimum margin time requirements between the primary/backup relay pairs.

Optimizing Hybrid Photovoltaic/Battery/Diesel Microgrids in Distribution Networks Considering Uncertainty and Reliability

Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated.

Preserving Privacy in Nested Peer-to-Peer Energy Trading in Networked Microgrids Considering Incomplete Rationality

With the high penetration of renewable energy resources, energy prosumers and microgrids can trade energy directly with each other in a hierarchical way. This paper develops a nested market clearing algorithm that alternately produces optimal trading results in both inter- and intra-microgrid markets. The inter-microgrid market refers to the energy exchange among microgrids in distribution network. The intra-microgrid market is the energy sharing among prosumers inside microgrids. We use a distributionally robust optimization approach that is based on prospect theory (PT) to account for the complexity of prosumers’ behavior. The prosumers’ irrational behavior refers to the incomplete rationality in the market. The objective uncertainty and risk preference inside microgrids are modeled uniformly, based on which the alternating direction multiplier method (ADMM) algorithm is employed to solve this problem while preserving privacy. By filtering the marginal energy prosumers in each microgrid, the privacy of microgrids in the inter-microgrid market is preserved as well. Moreover, we prove that the proposed nested market clearing algorithm can reach the global Nash equilibrium. Numerical simulations demonstrate that the condensed equilibrium representation (CER) successfully selects energy prosumers from an intra-microgrid market to participate in an inter-microgrid market. The impact of risk preference parameters on the total utility is also analyzed.

Resilient cooperative control for optimal current sharing and voltage regulation of microgrid-based distribution network under FDI attacks

In this study, the security secondary control problems are considered for optimal current sharing and voltage restoration of a microgrid distribution network under false data injection (FDI) attacks. To solve these problems, a resilient secondary control method is provided. Specifically, a resilient secondary controller is designed by introducing an adaptive parameter based on the adaptive technique. Then, a theoretical analysis method is provided to show that the designed resilient secondary controller can ensure optimal current sharing and voltage regulation under FDI attacks.

Stochastic day-ahead optimal scheduling of multimicrogrids: an alternating direction method of multipliers (ADMM) approach

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only reduces the burden of calculations, but also increases the privacy of entities. Wind turbines as renewable based generators are assumed to participate in this system. To model the uncertainties of these units, chance-constrained programming is employed. Also, due to clean output of hydrogen storage systems and fuel cells, the inclination for using these systems has expanded. Simulations on the test case study demonstrate the applicability and performance of the proposed methodology. Considering the reliability level as 0.9 results in 12, 989$ in the test case. By considering the reliability level as 0.8, the operational cost becomes 11, 712$ which shows a reduction of 1277$ which is achieved by jeopardizing the system reliability by 10%.

Reducing Microgrids Integration Complexity in Distribution Networks Considering Bidirectional Power Flow in SFCLs

In this paper, investigations are carried out to explore the application of the well-known unique bidirectional power flow nature of superconducting fault current limiters (SFCLs) to reduce the complexities of integrating microgrids into distribution networks. More specifically, their adverse impact on the coordination of existing protection of distribution networks, as well as the complexity of setting their internal protection to handle the change in fault current due different modes of operation. In this context, the capability of the proposed SFCL scheme is tested while demonstrating the tandem operation of the utility distribution network and microgrid protection systems and problems related to sensitivity and selectivity are addressed. In addition, different fault contingencies are considered, namely, faults located on the incoming main-lateral connecting the microgrid to the distribution network (where the fault is between the distribution substation protection and the microgrid) and on an adjacent main-lateral from the same main feeder (where the microgrid is located between the distribution substation protection and the fault). Moreover, other faults are located inside the microgrid on a local-line and at a local end-user. Results have shown the efficiency of the proposed SFCL scheme in reducing the short circuit contributions of both the microgrid and the utility network within the coordination limits depending on the direction of flow into the point of connection (PoC). Thus, it allowed a safe continuous integration of microgrids in distribution networks during faulted conditions, while, simultaneously permitted the microgrid to utilize a single protection setting to handle the changes in short circuit current levels when transitioning between grid-connected and islanded modes of operation. The time-domain simulation studies are conducted using PSCAD/EMTDC software.

ESTABLISHMENT AND PLACEMENT OF A MULTI-PURPOSE PHASOR MEASUREMENT UNIT TO IMPROVE PARALLEL STATE ESTIMATION IN DISTRIBUTION NETWORKS

Today, microgrids in distribution networks are in dire need of improvement to cope with economic challenges, human losses, and equipment placement issues. Today, there is the issue of scattered resources in distribution systems, which has created many problems in the areas of environment, economy, and human and animal losses. The most important challenge in this section is the existence of voltage and frequency fluctuations during the occurrence of possible events such as severe load changes or errors in distribution networks. Having such a big problem can call a distribution network into question and destroy it. Therefore, it is necessary to provide an optimal method that can meet and cover these challenges. For this purpose, the present research deals with the problem of establishing and placing a multifunctional phasor measurement unit to improve the parallel state estimation in distribution networks, which offers a control approach. This approach determines the time of occurrence of internal and external disturbances after using the phasor unit. The approach of this research is to use a neural-fuzzy method because there is uncertainty in the distribution network due to the mentioned challenges, and training in the system is needed to accurately deploy and place possible errors. Do not occur. When setting up and placing the phasor measuring unit, the most important issue is the proper distribution of the load in the distribution network. The simulation results in the MATLAB / Simulink environment show the improvement of the results according to the proposed approach.Keywords: Distribution Network, Neural-Fuzzy Network, Optimal Load Distribution, Parallel State Estimation, Phasor Measurement Unit.

ESTABLISHMENT AND PLACEMENT OF A MULTI-PURPOSE PHASOR MEASUREMENT UNIT TO IMPROVE PARALLEL STATE ESTIMATION IN DISTRIBUTION NETWORKS

Today, microgrids in distribution networks are in dire need of improvement to cope with economic challenges, human losses, and equipment placement issues. Today, there is the issue of scattered resources in distribution systems, which has created many problems in the areas of environment, economy, and human and animal losses. The most important challenge in this section is the existence of voltage and frequency fluctuations during the occurrence of possible events such as severe load changes or errors in distribution networks. Having such a big problem can call a distribution network into question and destroy it. Therefore, it is necessary to provide an optimal method that can meet and cover these challenges. For this purpose, the present research deals with the problem of establishing and placing a multifunctional phasor measurement unit to improve the parallel state estimation in distribution networks, which offers a control approach. This approach determines the time of occurrence of internal and external disturbances after using the phasor unit. The approach of this research is to use a neural-fuzzy method because there is uncertainty in the distribution network due to the mentioned challenges, and training in the system is needed to accurately deploy and place possible errors. Do not occur. When setting up and placing the phasor measuring unit, the most important issue is the proper distribution of the load in the distribution network. The simulation results in the MATLAB / Simulink environment show the improvement of the results according to the proposed approach.

Resilience enhancement strategy using microgrids in distribution network

We propose a restoration strategy using microgrids for restoring power supply to critical loads after an extreme event and thereby enhancing the resilience of the distribution power grid. The limited capacities of distributed generators (DGs) within the microgrids and those of intermittent energy sources such as wind and photovoltaic power are considered. An enhanced strategy model of the distribution network is established for maximizing the power supply to critical loads. Firstly, the importance of the load is quantified by using the analytic hierarchy process (AHP) and the model of the microgrid output is further improved. In the demand response mechanism, an interruptible load is used to suppress the fluctuation in the distributed power output. Secondly, piecewise linearization method is applied to address the power flow constraints. Then, the resilience enhancement model of the distribution network is transformed into a mixed integer quadratic programming problem. The CPLEX solver is adopted to solve the above problem on the MATLAB platform. Finally, the proposed method is verified by applying it to practical scenarios.

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility.

A Bi-Layer Multi-Time Coordination Method for Optimal Generation and Reserve Schedule and Dispatch of a Grid-Connected Microgrid

With the integration of more microgrids in distribution networks, its optimal autonomous operation becomes more important to reduce its operating cost and its influence on the main grid. This paper proposes a bi-layer multi-time coordination method for optimal generation and reserve schedule and dispatch of a grid-connected microgrid to reduce the impact of uncertainties of renewable sources, loads, and random component failures on power balance, operating costs, and system reliability. The reserve is refined into positive and negative reserves related to power shortage and power surplus. In the days ahead schedule layer, generating units are committed, and relaxed bidirectional reserve boundaries are predicted for the next day. In the real-time dispatch layer, generation output is dynamically adjusted and the reserve is dispatched using a successive approximation based on real-time data. A test microgrid is analyzed to illustrate the effectiveness of the proposed approach.

Optimal Transactive Market Operations With Distribution System Operators

Distribution system operator (DSO) has traditionally been responsible for the reliable operation of power distribution systems. The advent of distributed energy resources (DERs) and microgrids in distribution networks has required a new platform for DSOs. The new DSO, which is for the most part an independent agent, is responsible for aggregating a widely dispersed DERs (which include small thermal generation units, energy storage, and demand response) and flexible loads in electricity markets. DSO coordinates and balances the transactive dispatch of supply and demand at the distribution level and links wholesale and retail electricity markets. In this paper, a framework for the day-ahead transactive market is proposed which includes end-to-end power system participants starting from the bulk power ISO and ending at DSO, which includes the management of retail customers with small loads. In this paper, DERs are considered in a local distribution area (LDA). The day-ahead transactive scheduling of LDA is modeled as an MILP and solved using the CPLEX solver. This paper offers numerical results considering a DSO that is responsible for the optimal and secure operation of LDA with microgrids. The simulation results show that the proposed DSO framework in a transactive market can efficiently reduce the supply cost of LDA and increase the GenCos’ payoff considering the optimal power exchange with the ISO.

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.

Privacy-preserving optimal scheduling of integrated microgrids

The increasing penetration of microgrids in distribution networks, as a viable option for end-use customers to increase load-point reliability and power quality, will result in formation of many interconnected microgrids in a not so far future. This paper considers a case in which multiple microgrids are geographically close and electrically connected, and studies anticipated interactions among these microgrids and also between the microgrids and the utility grid, during grid-connected and islanded operation modes. A model for the optimal scheduling of integrated microgrids is further proposed. The model is first developed with the objective of minimizing the aggregated operation cost and is accordingly decomposed into individual optimal scheduling problems using the Lagrangian relaxation method to take prevailing privacy issues into account. The microgrids capability in operating in the islanded mode for multiple hours is scrutinized by a T–τ islanding criterion. Numerical simulations exhibit the merits and the effectiveness of the proposed model via simulations on a system of integrated microgrids.

Optimum Shunt Capacitor Placement in Multimicrogrid Systems With Consideration of Islanded Mode of Operation

Appropriate implementation of multimicrogrid systems in distribution networks is dependent on the development of new planning methodologies that take into account the special operational characteristics of microgrids. This paper proposes a new algorithm for optimum shunt capacitor placement in multimicrogrid systems with consideration of islanded mode of operation. The cost function of the proposed optimization technique consists of three terms in the planning process: 1)the cost of power and energy losses when multimicrogrid systems operate in normal grid-connected mode; 2)the cost of capital investments of the installed shunt capacitors; and 3)the customers cost of interruption when microgrids fail to operate in islanded mode due to shortage of reactive power and/or voltage violations. Appropriate power flow and probabilistic models for distributed and renewable energy resources (DER) and loads have been incorporated in the optimization problem to provide proper representation for microgrids during both grid-connected and islanded modes of operation. A genetic algorithm (GA) has been utilized to solve the problem and several case studies have been conducted to validate the proposed capacitor placement methodology. The results show that the new methodology can facilitate successful deployment of microgrids in distribution networks.