Optimal Scheduling Of Microgrid Research Articles

Research on Improved BBO Algorithm and Its Application in Optimal Scheduling of Micro-Grid

Aiming at the cooperative optimization problem of economy and environmental protection of the traditional microgrid, including micro gas turbine and diesel engine, carbon capture and storage, and a power to gas system which can consume wind and light and deal with carbon dioxide, is introduced, and three optimization scheduling models of the microgrid based on improved BBO algorithm are proposed. Firstly, a micro-grid with a power to gas system is constructed, and an optimal scheduling model is built which takes into account the system operation cost, environmental governance cost and comprehensive economic benefit. Secondly, the ecological expansion operation is introduced, an improved BBO algorithm is explored by improving the mobility model, and its convergence is derived in detail. Finally, the microgrid system energy optimization scheduling is realized based on the improved BBO algorithm. Compared with the scheduling model that only considers the operation cost or pollution gas control cost, the total cost of the comprehensive economic benefit scheduling model is reduced by 15.5% and 5.5%, respectively, which reflects the reasonableness of the scheduling model and the effectiveness of the improved algorithm.

Microgrid Optimal Scheduling Incorporating Remaining Useful Life and Performance Degradation of Distributed Generators

This study presents a novel microgrid optimal scheduling strategy, which considers the degradation of distributed generators (DGs). DGs affected by degradation exhibit reduced generation efficiency and capacity. Operating the microgrid without consideration of these impacts increases operating and management costs and reduces reliability and stability. To overcome this problem, we developed a new optimal scheduling strategy that considers the degradation of DGs. This study focuses on a permanent magnet synchronous generator, which is widely used as DGs recently, and on the degradation of stator insulation, which is one of the most frequently occurring forms of degradation. The reduction in performance caused by insulation degradation is analyzed using finite element analysis and is integrated into the optimal scheduling strategy. We demonstrate that the proposed strategy operates a microgrid more economically through a reduction of total operating costs, achieved by adaptively accounting for the increase in operating costs and the reduction of capacity arising from degradation. In addition, the sudden shutdown of a DG can be prevented by predicting its remaining useful life and incorporating it into the optimal scheduling strategy. The effectiveness and feasibility of the proposed strategy are confirmed using various case studies.

Optimal Energy Management of Microgrids Using Quantum Teaching Learning Based Algorithm

Quantum inspired computational intelligence is gaining momentum in the interest of enhancing the performance of existing metaheuristic optimization while solving multi-dimensional nonlinear problems. The microgrid optimal energy scheduling is one such problem that involves multiple distributed energy resources (DER) with volatile characteristics and proficient energy management is essential for their coordination and reducing global carbon emissions. Relatively very few works in the existing literature have attempted to solve this problem using quantum-based algorithms. In this article, a stochastic framework associated with the Quantum Teaching Learning-based optimization (QTLBO) algorithm is devised for the first time to optimize energy flow in the microgrids. Four scenarios concerning seasonal variations are chosen to address the uncertainties related to generated power from DERs with better accuracy. The day-ahead optimum power scheduling configuration of DERs is evaluated for each scenario. The performance of QTLBO is assessed on a grid-connected microgrid network and compared with existing metaheuristic algorithms such as the Real-coded Genetic Algorithm, Differential Evolution, and TLBO. The obtained simulation results prove the superiority of QTLBO in terms of convergence and achieving a global optimum solution by overcoming premature convergence. Further, the proposed stochastic framework is helpful to attain techno-economic benefits to both customers and market operators.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

Renewable resources and storage systems stochastic multi-objective optimal energy scheduling considering load and generation uncertainties

Average household electricity use and industrial branches electrical energy consumptions are on a rising trend these days. Actually, utilization of fossil fuels for electricity generation purposes, results in contamination of the environment. Besides, loss of resources may be encountered owning to the persistent consumption of the energy produced through this process. Hence, the mentioned concerns necessitate the existent generations substitution by renewable resources obtained energies, for instance wind and solar, which can be accounted as a reasonable approach. However, one of the complications ahead is modeling of the uncertainty for these resources, accompanied by their random nature aggravating the resultant planning and prediction. Accordingly, micro-grid optimal scheduling in presence of renewable resources for energy production and storage systems is assessed in this study, assuming the case in which micro-grid is connected to the main grid. The simulations are devoted to mixed-integer linear programming (MILP), which are performed via GAMS software. With the help of scheduling a central control system in the studied micro-grid, a virtual power producer (VPP) is capable of controlling the load as well as managing optimal production of the energy. According to the load variable and unpredictable behavior, in addition to the diesel generator, some other renewable resources are employed for load management and control. Again, for uncertainty modeling associated with solar and wind resources, probabilistic schemes are evaluated, and besides, stochastic programming is designated. Moreover, the generated pollution of the units is also modeled. As a final step, a comparison is made between the operation cost results achieved by the proposed technique and those derived from genetic algorithm (GA) in the studied micro-grid, validating the correctness of the scheme.

Optimal Scheduling of Microgrid Considering the Interruptible Load Shifting Based on Improved Biogeography-Based Optimization Algorithm

A microgrid is an efficient method of uniting distributed generations. To ensure the applicability and symmetry of the microgrid, the environmental benefits and economic costs are considered to comprehensively model the optimal operation of the microgrid under the grid-connected operation mode, at the same time, considering the effect of interruptible load on the operating cost of the microgrid, the power shifting for interruptible load is attempted on the basis of battery storage capacity. By adaptively adjusting the migration rate using the habitat suitability index of a normalized individual and adding a certain differential perturbation to the migration operator of the migration mechanism, an improved biogeography-based optimization algorithm is proposed and the microgrid optimization dispatching algorithm based on the improved biogeography-based optimization is applied. The advancement and effectiveness of the proposed algorithm and model is verified by the example, and the simulation results indicate that the implementation of the power dispatching scheme proposed in this paper can effectively reduce the total cost of the system. Moreover, the proper consideration of shifting interruptible load, the effective load management and guiding the electricity consumption behavior of users are of certain significance for optimizing the utilization of renewable energy and improving the system efficiency and economy.

Optimal scheduling of combined cooling, heating, and power microgrid based on a hybrid gray wolf optimizer

ABSTRACT This study aims to reduce economic cost and pollutant emissions of microgrid while satisfying the loads demand and system constraints. A combined cooling, heating and power microgrid model is established integrated with photovoltaic power generation unit and energy storage system. A hybrid grey wolf optimizer is proposed by introducing chaos strategy, mutation strategy and levy strategy into grey wolf optimizer to deal with the shortcomings of mathematical methods and original intelligent optimization methods in microgrid optimal scheduling. The proposed method is verified from a case study and compares with the original grey wolf optimizer. The hybrid grey wolf optimizer reduces the daily economic cost, environmental cost and comprehensive cost of microgrid system by 9.9 $, 34.3 $ and 20.27 $, with the reduction rates of 1.20%, 10.99% and 3.27%. This study is beneficial for alleviating environmental pollution and energy crisis, and promotes economic production and sustainable development.

Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques

An increasing demand in electric power consumption has clearly led to an exhaustion of alternating energy resources. Undoubtedly, it has harmful environmental effects. Hybrid energy and Micro grid can solve this kind of problem. The concept of micro grid is quite significant in cases where transmission of electric power is nither feasible nor profitable. An efficient scheduling of micro grid is able to meet load demand without shedding any load and the optimization is required to make it profitable and eco-friendly. In this regard this work implements a twenty four hours based environmental/economic scheduling of distributed generating units with renewable energy sources in a micro grid connected with main grid .This work proposes a framework for optimal scheduling of micro grid which minimize the cost of generating units as well as emission. Particle Swarm Optimization technique has been employed to solve this problem. Weighting factor is used for optimization in multi-objective framework where both cost and emission are minimized simultaneously. In this paper, a comparative study of employing different types of Particle Swarm Optimization has been made where Hierarchical Particle Swarm Optimization (HPSO) performs better incorporating different constraints. The results of proposed Particle Swarm Optimization method are compared and verified with results of others method which is recently employed. Finally, the comparative study indicates that proposed method gives superior solution than previous method in case of operating cost and emission.

Optimal Power Flow Algorithm Based on Second-Order Cone Relaxation Method for Electricity-Gas Integrated Energy Microgrid

Due to the existence of nonlinear constraints, it is difficult to solve the power flow directly. This paper proposes a microgrid optimal scheduling strategy using second-order cone relaxation method to realize linear transformation, so as to minimize the total cost of the microgrid. Firstly, a microgrid system model of electricity-gas integrated energy is established, and the nonlinear constraints of branch power flow are transformed by the second-order cone relaxation method. Then, based on the microgrid model, the application conditions of the second-order cone relaxation transformation method are studied, and the optimal scheduling strategy with the total cost of microgrid as the objective function is proposed. In addition, in the case that the microgrid system does not meet the application conditions of second-order cone programming, the optimization problem is solved by increasing the line loss. Finally, an example is given to verify the effectiveness of the proposed method.

Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling

This letter proposes an enhancement for artificial neural network (ANN) using particle swarm optimization (PSO) to manage renewable energy resources (RESs) in a virtual power plant (VPP) system. This letter highlights the comparison of the ANN-based binary particle swarm optimization (BPSO) algorithm with the original BPSO algorithm. The comparison has been made upon searching the optimal value of the number of nodes in the hidden layers and the learning rate. These parameter values are used in ANN training for microgrid (MG) optimal energy scheduling. The proposed approach has been tested in the VPP system covering MGs involving RESs to minimize the power and giving priority to sustainable resources to participate instead of buying power from the utility grid. This model is tested using real load demand recorded for 24 h in Perlis state, the northern part of Malaysia. Besides, real weather condition data are recorded by Tenaga Nasional Berhad Research solar energy meteorology for a 1-h average (e.g., solar irradiation, wind speed, battery status data, and fuel level). The results show that ANN-PSO gives precise decision compared with BPSO algorithm, which in turn prove that the enhancement for the neural net reaches the optimum level of energy scheduling.

Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning

Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based on Deep Deterministic Policy Gradient (DDPG) and Transfer Learning (TL). This method uses Reinforcement Learning (RL) to learn the scheduling strategy and accumulates the corresponding scheduling knowledge. Meanwhile, the DDPG model is introduced to extend the microgrid scheduling strategy action from the discrete action space to the continuous action space. On this basis, this paper holds that a microgrid optimal scheduling TL algorithm on the strength of the actual supply and demand similarity is proposed with a purpose of making use of the existing scheduling knowledge effectively. The simulation results indicate that this paper can provide optimal scheduling strategy for microgrid with complex operation mechanism flexibly and efficiently through the effective accumulation of scheduling knowledge and the utilization of scheduling knowledge through TL.

Resiliency-oriented optimal scheduling of microgrids in the presence of demand response programs using a hybrid stochastic-robust optimization approach

Natural incidents with a low occurrence probability and high impacts like windstorms and earthquakes, threaten the optimal operation of the distribution networks. Hence, some operational solutions are required to improve network resiliency. Microgrids (MGs) provide a proper solution for the resiliency enhancement of distribution networks to reach the optimal resilient operation under such incidents. Also, the inherent uncertainties make the resilience-oriented optimal operation of MGs more critical and should be modelled by efficient uncertainty modelling tools. In this paper, a hybrid stochastic-robust optimization (HSRO) approach is used to get the optimal scheduling of an MG under normal and resilient operation mode. The impact of the upstream grid price uncertainty on the optimal scheduling of MGs is modelled using the robust optimization approach. Other dominant uncertainties, including the wind power, photovoltaic (PV) power, and active/reactive electrical loads, are also modelled by stochastic optimization via generating a set of relevant scenarios. In addition, adjustable and interruptible demand response programs (DRP) are implemented to improve the resilient operation of MG under incidents. The proposed approach tries to improve the MG operation under uncertainties during the resilience and normal mode of network. Furthermore, this paper concentrates on the robust and normal strategies adopted for the resilience-oriented scheduling of MGs. The effectiveness of the proposed framework is evaluated through a large-scale MG test system.

A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids’ components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

This paper presented an effective technique for considering optimal scheduling of microgrid (MG) with islanding constraints. The proposed optimal scheduling approach is the combination of both the modified dragonfly algorithm (MDA) and whale optimization algorithm (WOA)-based approach named as (MDAWO). In this work, the searching conduct of the dragonflies is altered by the effective WOA approach. This WOA is used to the optimal scheduling of MG and also significantly reduces the computational burden. In this paper, the MDAWO is the appraisal technique used to set up the definite schedule of the MG coupling subject to the power assortment. The equality and inequality constraints are utilized to characterize the target capacity of a projected approach using the system information. Batteries act as an energy source to adjust the MG with continual running at an unfaltering and stable output control. Finally, the performance of the proposed technique is executed through the MATLAB/Simulink working platform with two different scenarios. With these two different scenarios, the proposed technique performance is compared with existing techniques such as dragonfly algorithm, firefly and gravitational search algorithm. Furthermore, the statistical analysis of the proposed technique based on cost and fitness are analyzed. Numerical simulations demonstrate the effectiveness of the proposed MG optimal scheduling model and explore its economic and reliability merits.

Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System

Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

Optimal scheduling of cogeneration microgrid with wind energy and energy storage devices

Aiming at the optimal scheduling of cogeneration microgrid, a microgrid structure including wind energy, energy storage device, solar thermal power station, gas turbine and waste heat boiler is proposed. Based on the uncertainty of wind power output and load forecasting, according to beta distribution wind power output and normal distribution load forecasting, the CHP microgrid optimal scheduling model of photothermal power station is established and solved by genetic algorithm. Finally, the minimum operation cost of the system under different confidence levels is discussed. The results show that the minimum operating cost of the system can be significantly reduced by adding wind power generation into the microgrid structure, and the relationship between the confidence level of load forecasting and the minimum operating cost of the system is studied.

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.

Optimal Management of Transactive Distribution Electricity Markets With Co-Optimized Bidirectional Energy and Ancillary Service Exchanges

This paper develops a novel co-optimized distribution system management algorithm for the distribution system operator to optimally manage the ever-increasing penetration of networked microgrids that are participating in the transactive distribution market as prosumers. For the first time, we evaluate how the incorporation of joint bidirectional energy and ancillary service exchanges could quantitatively increase the operation economy, flexibility, and reliability for both the microgrids and the distribution grid. We formulate the joint distribution system management problem using a bilevel programming approach, in which the upper level is a distribution system optimal management problem for the distribution system operator and the lower level is a microgrid optimal scheduling problem for each participating microgrid operator. Uncertainties from renewable energy generation and responsive loads within the distribution system are also incorporated in the problem formulation. The bilevel stochastic programming model is then reformulated as a single mixed-integer model to solve. The simulation-based case study supports that the proposed management scheme is capable of enhancing energy independency, system-wide efficiency, operational reliability, and economy of the distribution system in comparison with conventional transactive market management schemes.

Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

<p>This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.</p>

Risk-Based Performance of Combined Heat and Power Based Microgrid Using Information Gap Decision Theory

In this paper, to get optimal scheduling of microgrids (MGs) in short-term, info-gap decision-theory (IGDT) is applied to assess load uncertainty. In order to provide a comprehensive load uncertainty study, best and worst possible conditions are evaluated using IGDT's functions of opportunity and robustness, respectively, in which risk-taker and risk-averse strategies are obtained while deterministic case study is also carried-out under the risk-neutral strategy. Real-time pricing (RTP) and time-of-use pricing (TOU) of demand response program (DRP) are applied to peak-load management. For each strategy, three cases as without DRP, TOU-DRP, and RTP-DRP are investigated. According to the results in the risk-neutral strategy, by applying TOU-DRP and RTP-DRP, operating cost is reduced about 2.5% and 6.6%, respectively. In the risk-averse strategy, by considering MG operating cost equal to $6,000, the MG will robust against load uncertainty up to 18.37%, 21.52%, and 24.82% for without DRP, TOU-DRP, and RTP-DRP cases, respectively. In the risk-taker strategy, MG operating cost for 28% of load reduction, operating costs will be equal to $3,698.12, $3,605.21, and $3,338.87 for without DRP, TOU-DRP, and RTP-DRP, respectively, in comparison with the risk-neutral strategy.