Optimal Operation Of Microgrid Research Articles

Study on bi-layer optimal operation of new energy microgrid with P2G

To improve the utilization of new energy sources and reduce economic costs, a micro-grid dispatching model based on new energy prediction with P2G is proposed. This paper consists of two stages. The first stage is to optimize the model of support vector machine using particle swarm algorithm to predict the data of wind power and photovoltaic power generation based on several influence factors. The second stage is to use the data of wind power and PV output in the first stage and put them into the micro-grid model of this paper for optimal dispatching. This stage is solved by using CPLEX+YALMIP and divided into three scenarios for comparative analysis. The conclusion is that the micro-grid containing P2G has effectively improves the economy and the capacity of consuming renewable energy. The bi-layer optimization of this paper, with the first stage improving accuracy and the second stage optimizing dispatch, not only greatly promotes the smooth and reliable operation of micro-grids, but also reflects the systematic nature of micro-grid modelling.

Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections

Multi-energy microgrids comprise various energy sources such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids enhance energy efficiency and reduce carbon dioxide emissions. In this work, a novel and detailed mixed-integer nonlinear programming (MINLP) model is proposed to compare the effects of carbon emission taxing and cap and trade system on the optimal equipment selection and scheduling under the regulatory effects of the Paris Agreement. The candidate equipment to be selected by the MINLP model includes two solar panel arrays, two wind turbine farms, two integrated gasification combined cycles, two combined heat and power units, two conventional generators, one biogenerator, one battery, two bio-gasifiers, and one power-to-gas system. The impact of carbon dioxide price, taken as 50, 75, and 100 $/ton, is investigated for both emission taxing and carbon dioxide cap and trade system. Equipment selections with the multi-period seasonal data and the carbon dioxide prices of 50 $/ton and 75 $/ton in stand-alone mode are the same for both cases without selecting renewable sourced generators. For the same conditions and with a carbon dioxide price of 100 $/ton, the MINLP model selects one wind turbine farm, one biogenerator, and one bio-gasifier. Moreover, the power-to-gas system is chosen in electricity grid-connected mode under emission taxing with the carbon dioxide price of 100 $/ton. For both emission taxing and cap and trade system, the trials using the seasonal data and the yearly average data with carbon dioxide price of 75 $/ton in stand-alone mode contributes to different equipment selections. Results show that carbon dioxide price may be a crucial parameter for equipment selection and must be taken into account in the design phase for optimal design and management. Finally, the MINLP solvers SCIP, BARON, and DICOPT, are compared in terms of CPU times and equipment selections.

Robust Optimization-Based Optimal Operation of Islanded Microgrid Considering Demand Response

This paper presents a new robust scheduling model for an islanded microgrid (MG) considering demand response. The model is expressed as a min–max bilevel optimization problem that tries to minimize the total costs of MG including operation cost of conventional distributed generators, energy storages, renewable energy sources (RES), cost of load shifting, and interruptible/non-interruptible load shedding in the worst situation of uncertainties. The uncertainties associated with renewable power generations and MG demand are modeled via robust optimization method. A hybrid method based on the genetic algorithm (GA) and mixed-integer programming technique is utilized to solve the bilevel optimization problem. The proposed model is utilized on a typical MG, and the outcomes are analyzed to show the effectiveness of the proposed method.

Optimal economic operation of microgrids considering combined heat and power unit, reserve unit, and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.

Research on optimal operation of grid-connected microgrid based on improved whale algorithm

In this paper, a small microgrid system is composed of wind turbines, photovoltaic generators, micro gas turbines, fuel cells and battery energy storage devices. In order to realize the optimal operation of the grid-connected microgrid, a multi-objective optimal operation model with economical and environmental friendliness is established. Secondly, an adaptive strategy is introduced for the whale optimization algorithm (WOA) to improve the optimization accuracy and convergence speed of the whale algorithm. The improved whale algorithm is used to solve the target model, and the optimal operation scheme of each distributed power source in the microgrid is obtained, and the simulation analysis results are compared to verify the rationality of the improved whale algorithm.

Optimal Operation of Standalone DC Microgrids Considering Renewable and Load Uncertainties

The rapid growth and development in DC microgrids (DCmGs), led system-level operation and control to be reformed continuously imbibing constraints and concerns that improve system operational performance. Focusing only on islanded DCmGs, quite a literature/work has been attempted in various perspectives to solve energy management problems. In light of this aspect, an optimal operation/control of standalone DCmGs, composed of the droop-regulated dispatchable type distributed generators (DGs) considering generation cost, voltage deviation, and current-sharing objectives has been proposed. The proposed work includes the uncertainties in network variables such as load demand, wind, and solar power generations. The scenario-based analytical method has been employed for modeling the uncertainties stated. For optimal operation, the droop parameters were solved by means of the heuristic-based multi-objective optimization technique, Dragonfly Algorithm (DA). The proposed methodology has been implemented on modified 6-bus and 33-bus test systems operated as DCmGs. Furthermore, the applicability of the proposed approach has been presented for the case study with battery energy storage system (BESS) scheduled DCmG network operation on a 24-hour time horizon. The obtained results have been compared with other optimization algorithms to validate the accuracy of the proposed approach.

Optimal Operation of Microgrids Based on a Radial Basis Function Metamodel

The optimal operation of a microgrid (MG) is a nonlinear multiconstraint problem. In addition to optimizing the output of different distributed generations (DGs) at the same time, the output of DGs at different times must be coordinated to achieve a balance between power supply and load demand. To solve the optimal operation problem of an MG, an optimal operation model to minimize user electricity cost and MG operation cost is proposed in this article. This model refines the load types and considers the scheduling elasticity of multiple types of home loads of end-users. It also quantifies the scheduling elasticity as the demand response based on the time-of-use price. Furthermore, to solve the problems of a complex solving process and long computation times for the MG optimal operation model, a global optimization algorithm based on a radial basis function (RBF) metamodel is proposed. The novelty of the proposed algorithm is that it adopts Latin hypercube sampling and an RBF to construct a metamodel. The constructed RBF metamodel is used to replace the proposed MG optimal operation model to estimate the function values, thereby avoiding calling the original complex objective function repeatedly. The proposed model and algorithm are tested on the modified IEEE 37-bus and 300-bus systems. The test results show that, the proposed model can effectively reduce the electricity cost of the end-users and enable the MG to obtain more electricity selling revenue. Meanwhile, the proposed algorithm can effectively reduce computational burden and time and has higher search efficiency of the global optimal solution and computational accuracy.

Distributed Energy Management for Zero-Carbon Port Microgrid

A zero-carbon port microgrid that integrates carbon capture power plants is proposed to build the green port and promote the achievement of the dual-carbon goal. To achieve the optimal economic operation of the port microgrid and reduce carbon emissions, an energy management model considering carbon trading mechanisms is established. Furthermore, a distributed energy management method is proposed for the zero-carbon port based on the alternating direction method of multipliers (ADMMs). The simulation results prove the effectiveness and accuracy of the proposed method, which can effectively improve the economy of the port microgrid and reduce the carbon emission of the port.

A Game-Theoretic Approach of Optimized Operation of AC/DC Hybrid Microgrid Clusters

To maximize the benefits of microgrid clusters, a general model and analysis method for studying the optimized operation of AC/DC microgrid clusters using non-cooperative games is proposed. This paper first establishes the optimized objective function of an AC/DC microgrid for economic operations. Based on the supply and demand theory, the dynamic adjustment mechanism of electricity price is introduced into microgrid clusters, and a game model for the optimal operation of multiple microgrids is established. The Nash equilibrium solution of the established model is obtained by iterative search algorithm, and the convergence of the Nash equilibrium solution is also proven. Finally, the validity and economy of the proposed model are verified by the actual case.

Optimal operation and scheduling of a multi-generation microgrid using grasshopper optimization algorithm with cost reduction

Optimal operation and scheduling of a multi-generation microgrid using grasshopper optimization algorithm with cost reduction

A Literature Review of the Control Challenges of Distributed Energy Resources Based on Microgrids (MGs): Past, Present and Future

Different types of distributed generation (DG) units based on renewable and non-renewable energy sources can create a local energy system in microgrids. The widespread penetration of distributed energy resources (DERs) has affected many power system issues, such as the control and operation of these networks. For the optimal operation of microgrids, optimal energy planning and management in the new space governing the distribution system requires extensive research and analysis. Getting acquainted with the latest research about the evaluation of the problems and challenges in the design of control systems plays an important role in providing a guidance map for researchers to find the recent challenges and propose new solutions. This paper tried to list the challenges of distributed generation sources for MG applications, opportunities, and solutions. These challenges are reported in hierarchical control strategies and power-sharing categories. Therefore, Model Predictive Control (MPC)-based approaches are reviewed for different recent control levels and power sharing strategies in a comprehensive and simple point of view. The performance comparison of MPC methods together and different allocated fitness functions and implementation algorithms are dedicated. Another hand, the potential of MPC methods to control inverters for increasing the reliability of the grid, which this feature could not be achieved by using conventional strategies, while has not been investigated by researchers widely, is introduced in a short review. Therefore, this paper shows an intersection guidance map for readers to facilitate future research works in these exciting and undiscovered fields.

Economic-environmental operation of an unbalanced microgrid including energy storage systems via semidefinite relaxation

This paper proposes a mathematical optimization approach based on semidefinite programing (SDP) for optimal operation of an unbalanced microgrid (MG) equipped with renewable energy resources (RERs) as well as energy storage systems (ESSs) including compressed air energy storage (CAES) and electric vehicles (EVs). The SDP converts nonlinear and non-convex models proposed for day-ahead optimal operation into a convex approximation, which is easily implemented by commercial software. An optimization problem is formulated such that it minimizes the costs related to operation and environmental effects limited to several technical limitations. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a stochastic optimal operation model based on Information Gap Decision Theory (IGDT) with risk averse strategy to overcome this information gap and to help Microgrid Operator (MGO). As demand response programs (DRPs), the shiftable load and time of use (ToU) are considered for enhancing the flexibility of MG. Employing the presented model in the 21-bus test system shows that CAES and EVs, as novel ESSs, have good capability to reduce the costs pertaining to operation and emissions in the day-ahead optimal operation.

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Lately, the integration of renewable energy sources (e.g., photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEVs). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this article, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads; 2) the DSTATCOM feature of photovoltaic and wind generation systems; and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e., initial and preset conditions of their state of charge, arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bilevel metaheuristic-based approach. The upper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEVs, respectively. Both energy losses through the lines and energy from the main grid are considered as subobjectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEVs to form an autonomous microgrid.

Stochastic energy management for a renewable energy based microgrid considering battery, hydrogen storage, and demand response

This paper presents a stochastic management algorithm to address the optimal operation of smart microgrids (MGs) in the high presence of stochastic renewable energy resources. The proposed management algorithm aims to find a day-ahead optimal operation of the renewable energy resources, including wind turbine, photovoltaic (PV) unit, Fuel Cell (FC), electrolyzer, microturbine, and energy storage that simultaneously considers the participation of the smart homes in demand response programs. The presented stochastic management is formulated as a mixed-integer linear programming (MILP) problem solved through a stochastic programming approach. Two different energy storage devices, i.e., battery and hydrogen storage tank, are modeled, and their operations are compared. Various uncertainties (e.g., wind speed, solar irradiance, demand, and electricity price) are modeled, where the scenario-based approach is applied to find a finite number of scenarios. The efficiency of the presented algorithm is verified in four scenarios to evaluate the effect of hydrogen storage and demand response program. Finally, it is shown that applying both hydrogen storage and demand response program can benefit the MG from the economic perspective and results in a significant reduction in the total cost.

Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.

A scenario-based stochastic model for day-ahead energy management of a multi-carrier microgrid considering uncertainty of electric vehicles

This paper presents a scenario-based stochastic management algorithm to deal with scheduling the optimal operation of a multi-carrier microgrid (MG). The proposed framework of the multi-carrier MG comprises of wind turbine (WT), photovoltaic (PV) panel, fuel cell (FC), microturbine (MT), boiler, combined heat and power (CHP) unit, electrical load, thermal load, hydrogen load, electrical energy storage, H2 storage, and thermal storage. To cope with the uncertainties arisen by the renewable resources, electricity price, electrical load, and electric vehicle (EV), we proposed a scenario-based approach to model the uncertain parameters (wind speed, solar irradiance, electricity price, electrical load, EV arrival time, EV departure time, and EV traveled distance). The proposed algorithm initially estimates the uncertain parameters through fitting a probability distribution function (PDF) with the time series of historical uncertain parameters. Then, the scenario generation and reduction approach is applied to find the finite scenarios according to the fitted PDFs. The stochastic management algorithm is formulated as a cost minimization problem, where the demand response (DR) program is embedded in the formulation. The proposed framework is tested on a sample multi-carrier MG for two cases, namely, the application of stochastic energy management for a Multi-carrier MG with and without DR. Also, we perform a comparative study to show the benefits of the stochastic management algorithm along with a deterministic approach.

Multiagent-Based Distributed Coordination of Inverter-Based Resources for Optimal Operation of Microgrids Considering Communication Failures

This paper proposes the distributed coordination of inverter-based resources, to optimize the operational cost of a microgrid system. The microgrid is considered a multiagent system, which includes a distributed generator agent and energy storage system agent. A communication network is utilized to exchange information among agents. The issue of communication failures is addressed in the proposed strategy, to ensure the stable operation of the control system. A two-level hierarchical cooperative optimization system is proposed in this paper for distributed economic dispatch. The primary controller is responsible for the frequency and voltage regulations, and the secondary controller is implemented in a diffusion-based distributed control scheme, for optimal microgrid management. The proposed control strategy consistently maintains the optimal operation and frequency, even in the event of communication failures. A five-node multiagent system including a dispatchable agent is considered. Comparative studies with the conventional consensus strategy are represented, to prove the effectiveness of the proposed diffusion strategy. To demonstrate the practical feasibility of the proposed strategy, a controller hardware-in-the-loop testbed was developed for testing the proposed cyber-physical microgrid system, in which the controllers were implemented in multiple computers and the microgrid system was implemented in Opal-RT. The real-time experiment results showed the better cost optimization performance of the proposed diffusion strategy compared with the conventional consensus strategy.

Power-flow-based energy management of hierarchically controlled islanded AC microgrids

This paper presents a novel approach of employing hierarchical control to optimize the operation of islanded AC microgrids. The proposed method is an offline, centralized, power-flow-based energy management scheme which includes primary and secondary control dynamics in a modified power-flow formulation. The inner power-flow level maintains bus voltages and system frequency within the desired range and ensures power balance in the network. The outer optimization level ensures that the various components remain within their operational constraints, while optimizing a system-level objective. Two case studies are explored using a modified 14-bus medium voltage (MV) CIGRE benchmark microgrid to validate the proposed energy management algorithm. The first case includes minimization of conventional generator operating cost and renewable energy curtailment, both with linear and non-linear loads, and the second case includes minimization of conventional generator operating cost with load shedding. The results obtained from the case studies show the efficiency of the proposed energy management algorithm, and evidence its reliability for the optimal operation of islanded AC microgrids with multiple renewable energy sources.

Zonal-Based Optimal Microgrids Identification

Even though many studies have been deployed to determine the optimal planning and operation of microgrids, limited research was discussed to determine the optimal microgrids’ geographical boundaries. This paper proposes a zonal-based optimal microgrid identification model aiming at identifying the optimal microgrids topology in the current distribution systems through zoning the network into several clusters. In addition, the proposed model was developed as a mixed-integer linear programming (MILP) problem that identifies the optimal capacity and location of installing distributed energy resources (DERs), including but not limited to renewable energy resources and Battery Energy Storage Systems (BESS), within the determined microgrid’s boundaries. Moreover, it investigates the impact of incorporating the BESS in boosting the DERs’ penetration on the optimal centralized microgrid. Numerical simulations on the IEEE-33 bus test system demonstrate the features and effectiveness of the proposed model on identifying the optimal microgrid geographical boundaries on current distribution grids as well as its capability on defining the optimal sizes and locations of installing DERs within the microgrid’s zonal area.

Optimal operation of CCHP microgrids with multiple shiftable loads in different auxiliary heating source systems

This paper studies the optimal operation of integrated system from the perspective of the demand side load shifting, aims at adapting to the unbalance between energy demand and supply in the combined cooling, heating and power (CCHP) microgrid. A shiftable electrical load model is proposed, which combined with the time-of-use (TOU) price strategy and environmental benefits of solar and wind power. Based on the TOU price strategy, the shiftable load models are established according to the user’s room temperature comfort demand and the allowable fluctuation range of the domestic hot water’s temperature, respectively. Then the optimization model is constructed, which takes into account factors such as economy and environment, and solved by a CPLEX solver. Finally, in order to deeply study the influence of TOU price on the system, the influences of heating/cooling and electrical load shift on the CCHP microgrids with different auxiliary heating sources are studied. The results show that the influence of Shiftable loads in the system using an electric boiler as an auxiliary heating source is significantly better than using a gas boiler; the proposed shiftable load models can reduce the operational cost by up to 17.02%; and have better effect than the existing one that only considers TOU.