Optimal Operation Of Microgrid Research Articles

Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty

Owing to the increasing utilization of renewable energy resources, distributed energy resources (DERs) become inevitably uncertain, and microgrid operators have difficulty in operating the power systems because of this uncertainty. In this study, we propose a two-stage optimization approach with a hybrid demand response program (DRP) considering a risk index for microgrids (MGs) under uncertainty. The risk-based hybrid DRP is presented to reduce both operational costs and uncertainty effect using demand response elasticity. The problem is formulated as a two-stage optimization that considers not only the expected operation costs but also risk expense of uncertainty. To address the optimization problem, an improved multi-layer artificial bee colony (IML-ABC) is incorporated into the MG operation. The effectiveness of the proposed approach is demonstrated through a numerical analysis based on a typical low-voltage grid-connected MG. As a result, the proposed approach can reduce the operation costs which are taken into account uncertainty in MG. Therefore, the two-stage optimal operation considering uncertainty has been sufficiently helpful for microgrid operators (MGOs) to make risk-based decisions.

Optimal Operation of Unbalanced Microgrid Utilizing Copula-Based Stochastic Simultaneous Unit Commitment and Distribution Feeder Reconfiguration Approach

Currently, the microgrid operators try to operate this special type of the electrical grid in an optimal way due to the energy and cost saving and enhancing the other technical, environmental and economic aspects. Two of the most important tasks of operators to improve the efficiency of the microgrid are the optimal unit commitment and the distribution feeder reconfiguration. If these tasks are individually carried out, it may not lead to the optimal operation. Simultaneously, performing these two tasks in an unbalanced microgrid is a challenging multi-objective problem that this paper is faced with it. The assumed unbalanced microgrid has been equipped by two hybrid energy systems which include the dispatchable distributed generations that are fuel cell units and the non-dispatchable ones that are wind turbines and photovoltaic cells. The stochastic behavior of the non-dispatchable generation units and electrical demand is modeled by a stochastic copula scenario-based framework. The objective functions are minimization of the operational cost of the microgrid, minimization of active power loss, maximization of voltage stability index, minimization of emissions, and minimization of the voltage and current unbalance indices subject to diverse technical constraints. The proposed multi-objective problem is optimized by multi-objective covariance matrix adaption-evolution strategy (MOCMA-ES) algorithm, and a set of Pareto solutions is achieved. The best compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on an unbalanced 25-bus microgrid. The simulation results show the efficacy of the proposed model to optimize objective functions, while the constraints are satisfied.

Communication‐free optimal economical dispatch scheme for cascaded‐type microgrids with capacity constraints

The existing method cannot realise the optimal economical dispatch operation of cascaded-type microgrids with capacity constraints in a communication-free manner. To address this problem, a new optimal economical dispatch control scheme is proposed for the islanded cascaded-type microgrids without communications. Compared to the existing method, there are two prominent advantages: (i) global optimal economical operation with capacity constraints is realised; (ii) improved load voltage quality is obtained. Then, the stability of the proposed scheme has been analysed with a small-signal analysis method. Finally, the effectiveness of the method is verified by both simulations and experiments.

Optimal operation of water-energy microgrids; a mixed integer linear programming formulation

Increasing energy efficiency in water distribution systems is one of the most important aspects of a sustainable water infrastructure. Additionally, providing this energy from renewable sources is an essential step towards a cleaner production in energy infrastructures. Therefore in this paper, an optimization model is developed to minimize the energy consumption of a water-energy microgrid system. A day-ahead economic dispatch model is developed to minimize the daily cost of energy in the water-energy microgrid. The energy consumption of water system is minimized using tank’s and pump’s scheduling and operation, hydraulic factors, and daily demand. Particularly, the electricity consumption of the pump is minimized by adjusting its head gain and flow rate via a variable speed. The energy unit is composed of an aggregated conventional power generation, solar photovoltaic, wind generation, and battery energy storage system. To offer a global optimum for the proposed non-linear programming formulation, bivariate piecewise linear approximation is used to linearize the pump’s power consumption and gain, and conservation of energy-mass. In addition, univariate piecewise linear approximation is used to linearize the energy consumption function of conventional power generation systems. The optimization results of the mixed integer linear programming and mixed integer non-linear programming formulations for the studied water-energy microgrid system are compared and discussed. These models are developed to concurrently minimize the electricity consumption of a micro water-energy distribution network in two scenarios of (1) standalone water distribution system operation and (2) an integrated islanded micro water-energy system with electrical loads.

Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads

Based on the importance of device modelling for the optimal operation of combined cooling, heating and power (CCHP) microgrids and load shifting for the demand-side response, the partial load ratio (PLR) and constant efficiency (CE) models of the main devices on the system operation are studied. Nonlinear PLR models of gas turbines, boilers and chillers are constructed. A piecewise least squares linearization method is proposed. Shiftable load models of cooling, heating and electricity are established, and load shifting is carried out. System optimization models with multiple optimization objectives are constructed and solved by a CPLEX solver. To compare the effects of different equipment models on the optimal operation under load shifting, different targets and heat-to-electricity ratios are given. The results show that compared with the segment broken-line linearization and least squares linearization method, the proposed method reduces the standard error of the performance curve by at least 20.97% and 75.49%, respectively. With different optimization objectives, the positive advantage of load shifting is related to the heat-to-electricity ratio. Using different heat-to-electricity ratios, the sensitivity of the optimization results of the PLR models to the heat-to-electricity ratio is higher than that of the CE models. The optimal operational effect of load shifting on different objectives of the PLR model is significantly better than that of the CE model. The model selection of the gas turbine, heating recovery steam generator and electric chiller with either the PLR or CE model demonstrates a considerable impact on the system operational results.

A review of optimal operation of microgrids

The term microgrid refers to small-scale power grid that can operate autonomously or in concurrence with the area’s main electrical grid. 
 The intermittent characteristic of DGs which defies the power quality and voltage manifests the requirement for new planning and operation approaches for microgrids. Consequently, conventional optimization methods in new power systems have been critically biased all through 
 the previous decade. One of the main technological and inexpensive tools in this regard is the optimal generation scheduling of microgrid. As a primary optimization tool in the planning and operation fields, optimal operation has an undeniable part in the power system. This paper reviews and evaluates 
 the optimal operation approaches mostly related to microgrids. In this work, the foremost optimal generation scheduling approaches are compared in terms of their objective functions, techniques and constraints. To conclude, 
 a few fundamental challenges occurring from the latest optimal generation scheduling techniques in microgrids are addressed.

Optimal planning and operation of multi-carrier networked microgrids considering multi-energy hubs in distribution networks

In this paper, we propose a new scheme for designing multi-carrier networked microgrids. Various multi-energy carriers are utilized to form energy hubs which exchange energy with each other and the main electricity and gas networks. The proposed model considers renewable generation, diesel generators, fuel cells, energy storages, combined heat and powers, boilers, combined cooling heat and powers, and power to gas converters. The seasonal load patterns are considered to design the optimal capacity of combined cooling, heating, and powers. The main objective of the proposed model is to operate the integrated hubs with minimum cost, meet the consumer’s required energies, reduce the emissions, and create a bi-directional interaction between electricity, and gas networks using power to gas converters. While all electrical, mechanical, and technical constraints are satisfied. To achieve these improvements, industrial and residential hubs interact with other multi-carrier microgrids to determine the optimal operation of hubs. The proposed model is tested on a standard case study and results show when hubs integrate about the total daily cost of each microgrid has been improved about 200 $. Besides, the thermal energy not supply in microgrid 4 has been improved from 4.61 MWh to 0.45 MWh.

Optimal Operation of Microgrids with Load-Differentiated Demand Response and Renewable Resources

AbstractAs the level of renewable and distributed energy resources (DER) increases in power systems, there is a coincident effort to ensure ongoing reliability. Microgrids are likely to play a cent...

Integrated active/reactive power scheduling of interdependent microgrid and EV fleets based on stochastic multi‐objective normalised normal constraint

This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs in the proposed model, the authors investigate the effectiveness of EV's integration in the presence of the techno-economical objective functions. This work concentrates on the trade-off analysis of two conflicting objectives, including the economic objective of the MG's operation cost minimisation and the technical objective of the MG's voltage deviation. Besides, they consider several uncertainty sources, e.g. wind, EV and solar panel (PV) power provision, as well as market price fluctuations in the proposed model affecting the aforementioned techno-economic trade-off solution. The proposed model is a stochastic multi-objective mixed-integer non-linear programming problem where the authors apply the designed integrated framework on a modified IEEE 18-bus test case in GAMS software. Through numerical results, they demonstrate MG optimal operation changes due to different MGO priorities and study the positive effects of EVs integrated energy management on the bi-objective operation.

Economic energy and reserve management of renewable-based microgrids in the presence of electric vehicle aggregators: A robust optimization approach

Renewable energies and electric vehicles are introduced as promising solutions to save energy costs and reduce environmental impacts in microgrid systems. However, the uncertainty of such resources would necessitate the development of advanced management models for optimal operation of microgrids. To address this issue, this paper proposes a new model for energy and reserve management of microgrids in the presence of electric vehicles. To effectively cope with uncertainties, a robust optimization methodology is proposed and applied to handle the uncertain parameters. Furthermore, the optimization problem is transferred into a mixed-integer linear programming model to ensure achieving near-global and tractable solutions. The proposed model aims to coordinate energy serving entities a way that the microgrid social welfare is optimized while at the same time driving requirements of the electric vehicle owners satisfied reliably. The methodology is implemented to a microgrid and solved over a day-ahead scheduling time horizon. The trends of techno-economic-environmental indices confronting to the increasing level of uncertainty control parameter are evaluated thoroughly in four case-studies. A robust multi-objective model is developed to trade-off between social welfare and emission. The numerical results are verified through a Monte-Carlo Simulation strategy to demonstrate the impressiveness of the proposed methodology.

Robust MPC-based microgrid scheduling for resilience enhancement of distribution system

One of the requirements for a resilient distribution system with distributed generators (DGs) is to restore as many critical loads as possible during a power supply outage of the main power grid. During the outage period, the distribution network is divided into microgrids (MGs) and continuously provides power to loads. However, the uncertainty of renewable DGs’ output, which cannot be predicted perfectly, increases the difficulty of optimal operation of MGs. In this paper, an MG scheduling method is proposed based on robust model predictive control (RMPC) for the resilience enhancement of distribution networks. The DGs, energy storage devices, and switches are optimally coordinated to maximize load restoration. First, an RMPC-based two-layer MG scheduling model considering the worst-case scenario is established to schedule the structure of the MGs and the output of the DGs simultaneously. Furthermore, the original model is transformed into a single-layer programming problem using strong duality theory. The solution of the RMPC-based model can guarantee the reliability and feasibility of MGs within the predefined uncertainty sets of the DGs. The effectiveness of the proposed method is validated with the modified IEEE 37-node distribution test system.

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 Wasserstein based two-stage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties

Combined cooling, heating and power (CCHP) micro-grids are getting increasing attentions due to the realization of cleaner production and high energy efficiency. However, with the features of complex tri-generation structure and renewable power uncertainties, it is challenging to effectively optimize the operation of CCHP micro-grid. This paper proposed a novel Wasserstein based two-stage distributionally robust optimization (WTSDRO) model for the day-ahead optimal operation of CCHP micro-grid. The uncertainties of wind power (or other renewable energy sources with random power output) forecasting errors are modeled as an ambiguity set based on Wasserstein metric, which is assumed to contain all the possible probability distributions with a confidence level. In the first stage, CCHP micro-gird’s operation cost is minimized according to the forecast information. In the second stage, for hedging against the perturbation of random wind power outputs, flexible resources are adjusted under the worst-case distribution within the ambiguity set. Multiple demand response programs (DRPs) are integrated to make electrical, thermal and cooling loads controllable. Finally, a reformulation approach is proposed based on strong duality theory, which equivalently transforms the WTSDRO model into a tractable MILP framework. Simulations implemented on a typical-structure CCHP micro-grid are delivered to show that our proposed model: (1) is data-driven and keeps both of the conservativeness and computational time at relatively low levels, (2) reaches effective operation results in terms of cost optimization, wind power accommodation and waste heat utilization etc. Moreover, operation cost and CO2 emission can be further saved by integrating multiple DRPs.

Optimal scheduling for micro‐grid considering EV charging–swapping–storage integrated station

In this study, a micro-grid (MG) optimal operation model considering the electric vehicle (EV) charging-swapping-storage integrated station (CSSIS) is presented. According to the behaviour characteristics of fast charging users, the battery fast charging station (BCS) model based on queuing theory is established. In addition, the traffic flow model could be determined by the network topology and speed-flow relationship in order to propose the battery swapping station (BSS) model considering the departure schedule and route arrangement of electric buses (Ebs). Meanwhile, the CSSIS model is also introduced, which aggregates BCS, BSS, and the energy storage station into one unit. In the MG, which takes the outputs of wind turbines, photovoltaic cells, micro-turbines, CSSIS and the demand of original load into account, this study proposes an optimal operation model in order to minimise the daily total operation cost and the problem is formulated in the framework of model predictive control (MPC). MPC based on a multi-time scale coordinated approach, which is formed by day-ahead scheduling, intra-day rolling scheduling, and real-time feedback correction, is introduced to handle uncertainties in renewable energy and load. The results demonstrate the effectiveness and benefit of the optimal scheduling strategy.

A frequency-based economical-sharing strategy for low-voltage DC microgrids

This paper introduces a frequency-based control strategy to minimize the total generation cost in low-voltage dc microgrids. In dc microgrids, although the voltage is the only variable for controlling the outputs of sources, it is not a common variable for all sources due to the different line resistances. Hence, applying the conventional voltage-based droop control will not be able to share the load among the sources accurately. Within the context alluded above, to realize an accurate load sharing in dc microgrids, the communication-based approaches are employed. However, any link failure in these approaches may impair the controller functionality and consequently, render the whole system inoperable. To cope with the associated problems of the communication-based approaches, the frequency-based control strategy has been introduced for accurate load sharing in dc microgrids which operates without any communication network. This study proposes a frequency-based economical-sharing control strategy that has the capability to minimize the total generation cost and regulate the voltage of sources. The main advantage of this strategy over the previously addressed frequency-based strategies is providing an optimal combination of dispatchable sources to supply the required load on the grid. Through the employed control strategy, the incremental costs of all dispatchable sources will be equal in the steady state which causes the optimal operation of the dc microgrid. The small-signal modeling of the proposed strategy is also carried out to analyze the system stability and parameter sensitivity. A complete set of simulation studies is provided in PLECS software to verify the effectiveness of the proposed control strategy.

Approaches for optimal planning of the energy storage units in distribution network and their impacts on system resiliency — A review

In the recent decade, a significant increase in the penetration level of renewable energy sources (RESs) into the distribution grid is evident due to the world's shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system. RES based microgrids are the most favorable option available, especially to enhance resiliency. However, the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions. During extreme weather conditions, RES behavior is completely uncertain. Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions. To address these issues, many researchers proposed several methods to place energy storage units (ESUs) and microgrids (RES integrated), which can support critical loads at an optimal location in the distribution system during normal and extreme conditions, respectively. The aim of this article is to consolidate and review the research towards various approaches to formulate the problem (optimal location, allocation, and operation of ESU and microgrids to face regular and extreme weather condition) and tools to solve it for enhanced system flexibility and resiliency. Based on the review, a generalized methodology has been designed to adapt the inputs and address both conditions. At the end of the review, future aspects for ESU to strengthen resistance and resiliency of its own are presented, which can be helpful to further improve the reliability and resiliency of the distribution system.

Real-time Energy Management of Grid-connected Microgrid with Flexible and Delay-tolerant Loads

For optimal operation of microgrids, energy management is indispensable to reduce the operation cost and the emission of conventional units. The goals can be impeded by several factors including uncertainties of market price, renewable generation, and loads. Real-time energy management system (EMS) can effectively address uncertainties due to the online information of market price, renewable generation, and loads. However, some issues arise in real-time EMS as battery-limited energy levels. In this paper, Lyapunov optimization is used to minimize the operation cost of the microgrid and the emission of conventional units. Therefore, the problem is multi-objective and a Pareto front is derived to compromise between the operation cost and the emission. With a modified IEEE 33-bus distribution system, general algebraic modeling system (GAMS) is utilized for implementing the proposed EMS on two case studies to verify its applicability.

Contract-based utilization of plug-in electric vehicle batteries for day-ahead optimal operation of a smart micro-grid

Abstract The development of smart micro-grids makes the use of new and green technologies like plug-in electric vehicles more suitable. The plug-in electric vehicles can play a role in storing energy due to their network connection capability. The smart micro-grid can use this stored energy to reduce its operating cost. In this paper, a new mechanism based on the contractual agreements between the owners of plug-in electric vehicles and the smart micro-grid is proposed to provide the system's energy during the operating day. The proposed model enables plug-in electric vehicles parked in official parking to be integrated into the operation of smart micro-grid and earn revenue. The operation optimization is formulated as a two-stage stochastic mixed-integer linear problem and is implemented on energy management of a typical smart micro-grid as a case study. The simulation results confirm the effectiveness of the proposed design for both smart micro-grid and plug-in electric vehicles in the case of expected cost reduction. Also, the performance of the stochastic model for uncertainty handling is shown by the value of the stochastic solution.

Optimal operation management of grid-connected microgrids under uncertainty

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>