Scheduling Of Microgrid Research Articles

Day-ahead and intraday multi-time scale microgrid scheduling based on light robustness and MPC

An optimized microgrid scheduling model is established considering demand responses, forecast errors, and the effects of uncertainties in different scheduling stages. A day-ahead, intraday, multi-time scale economic scheduling method based on light robust optimization and model predictive control (MPC) is also proposed. In the day-ahead, long-time-scale scheduling stage, light robustness optimization is used to cope with low-frequency components in prediction errors and uncertainties, and mitigates the deviations between the day-ahead scheduling plan and the actual economic scheduling outcomes under source and load forecast errors and uncertainties. At the intraday, short-time-scale stages, the MPC tracks the day-ahead light robustness economic scheduling plan, considering the high frequency components of prediction errors and uncertainties, so as to achieve robust open-loop control, better tracking results, and better economy. Analytical results demonstrate the effectiveness of the method.

Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output

The grid connection of wind power and photovoltaics significantly increases the uncertainty of the operation and scheduling of power systems. The maximum power that can be transmitted in a particular time slot often fluctuates randomly around a certain value according to a certain probability distribution, and a prediction error occurs between the predicted and actual power. When the actual maximum power that wind power and photovoltaics can generate is less than the power arranged in the scheduling plan, a significant increase in operating costs will occur, as well as an increase in the frequency fluctuation of the microgrid. This may harm the system. To study the effects of the uncertainty of renewable energy output on microgrid dispatching, this paper divides power sources into basic load and frequency modulated (FM) power sources according to the FM characteristics and establishes a multi-objective optimal dispatching model of island microgrids considering the uncertainty of renewable energy output, using the economic benefit and frequency fluctuation rate of the microgrids as the optimization target. Additionally, this paper proposes a two-step optimal scheduling method based on power classification: First, the Monte Carlo method is used to estimate the expectation and variance of the objective function, and a certain number of samples are obtained through sampling. Second, the particle swarm optimization algorithm is used to determine the installation plan of the FM and base load power sources to minimize the average sample cost. Finally, a backpropagation neural network is used to construct the frequency modulation strategy of FM power sources. An analysis with examples shows that, in the context of many samples, the model and proposed two-step scheduling method are reasonable and effective.

Nash bargaining‐based cooperative game for distributed economic scheduling of microgrid with charging‐swapping‐storage integrated station

Nash bargaining‐based cooperative game for distributed economic scheduling of microgrid with <scp>charging‐swapping‐storage</scp> integrated station

A Joint Risk- and Security-Constrained Control Framework for Real-Time Energy Scheduling of Islanded Microgrids

High penetration of intermittent renewable energy sources (RES) and unexpected disruptions (e.g., natural disasters) are fundamental challenges which can threaten the secure operation of microgrids, especially during the islanded condition, with no support from the upstream grid. This paper introduces a hierarchical tri-layer min-max-min joint risk- and security-constrained model predictive control (RSC-MPC) framework for real-time energy scheduling of islanded microgrids (IMGs) under the influence of uncertainty and real-time time-varying contingency conditions. While the first layer processes a pre-scheduling day-ahead optimisation, the second layer detects the worst-case contingency conditions by maximising the load curtailment and the mismatch between pre-scheduling (i.e., first layer) and real-time operation. The third layer implements the corrective security measures to minimise the negative effect of contingency conditions while accounting to the cost associated with the risk of uncertainty in the forecasted inputs. The third layer also explores the economic effects of the RES’ uncertainty on the proposed RSC-MPC, considering the risk and energy procurement cost as conflicting objectives. The computational efficiency of the proposed hierarchical control system in terms of accuracy and processing time is guaranteed through a mixed integer conic programming model. The proposed RSC-MPC is tested in different case studies and its efficiency is validated by numerical results.

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.

A stochastic agent-based cooperative scheduling model of a multi-vector microgrid including electricity, hydrogen, and gas sectors

With increasing hydrogen usage, hydrogen subsystem should be considered in the multi-energy chain and a model is required that assumes current energy infrastructures, while preserving independent energy subsystems. In this study, a stochastic agent-based model is introduced for the coordinated scheduling of multi-vector microgrids considering interactions between electricity, hydrogen, and gas agents. Power to hydrogen (P2H) through electrolysis, hydrogen to power (H2P) through fuel cells, hydrogen to gas (H2G) through methanation, and gas to power (G2P) through distributed generation (DG) units are modeled to present the interactions among energy agents. The interactions in terms of shared variables and coupling constraints are described using augmented Lagrangian relaxation (ALR) and alternating direction method of multipliers (ADMM) to obtain three correlated optimization problems, preserving the privacy of energy sectors with minimum data exchange. An iterative process is accomplished among energy sectors to reach a consensus. Uncertainties in the wind turbine (WT) and photovoltaic (PV) power output, hydrogen vehicles (HVs), demands, and prices are captured using a stochastic method. To evaluate the proposed method, case studies are conducted using a multi-energy microgrid. The results verify that the microgrid is well scheduled and the interactions are accurately modeled, representing the effectiveness of the proposed method.

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs: it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs.

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.

Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm

Unpredictable nature of renewable energy sources puts power system operators in different conditions in terms of maintaining the system reliability. Microgrids (MGs) can provide an effective solution for the problems of grid-connected renewable energy sources due to their tuning capability and flexibility. In this study, a stochastic optimization strategy was proposed for participating in energy market operations considering demand response (DR). The results indicated the operating cost decreased when the MG implemented DR programs. Also, DR program could shift energy consumption from on- to off-peak hours and flatten the load curve. Therefore, a scheduling model was presented for the operation of energy carriers and reserves considering security constraints of power and natural gas grids in interconnected hubs as well as responsive load participation using the developed water wave optimization (WWO) algorithm. The objective function of this model aimed to minimize the operating cost of sources to supply electrical and thermal loads applied to the proposed MG. WWO is a meta-heuristic algorithm inspired by the behavior of water waves. The waves formed on the sea surface have complex, but interesting, relationships that can be used to solve optimization problems. In this algorithm, each problem solution is encoded as a water wave and undergoes changes in the problem search space based on three behaviors of propagation, refraction, and decay of water waves or problem solutions. The simplicity of the structure of this method, elitism and the ability to escape from local points due to the existence of different search operators and the mutation of generations have led to the use of this method. The results indicated a decline in operating costs through electrical and thermal responsive load participation and thermal energy storage system. Results of the proposed model revealed a correlation between the electricity price and natural gas consumption, indicating multi-carrier energy grids should be examined and optimized simultaneously.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration

As an important part of the Energy Internet, microgrid (MG) scheduling problem has always attracted great attention, especially under the background that large-scale penetration of electric vehicles (EVs) into buildings poses both opportunity and challenge to the MG energy management. This research presents a two-stage optimization strategy, for improving the economic and environment-friendly operation of MG considering EVs integration. In Stage 1, model of EVs under coordinate charging/discharging stimulated by time-of-use incentive mechanism is established, and the peak–valley hours are dynamically divided to obtain a load curve with minimized peak-to-valley difference (PVD). In Stage 2, aiming for the best tradeoff between the generation cost and pollutant emission, daily optimal scheduling of the MG generators is efficiently calculated according to the varying power demand. For enhancing the convergence, an advanced genetic algorithm with elite preservation strategy is employed. Two cases from a residential MG system is exploited to validate the proposed method, and the results show that firstly the power supply pressure could be obviously relieved owing to the load shifting effect of the coordinated vehicle-to-grid (V2G) service reflected by the decreased PVD (Case 1: from 76.16 to 54.35 kW, Case 2: from 76.25 to 46.19 kW); simultaneously, via applicable power planning of the MG components, cost saving and emission reduction can be both achieved (Case 1: ¥190.12 under V2G management compared to ¥281.66 under basic load, Case 2: ¥177.28 compared to ¥350.24), ensuring the feasibility of the control strategy, which promotes the economic-environmental and reliable operation of the MG system.

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

Active and Reactive Power Coordinated Two-Stage MG Scheduling for Resilient Distribution Systems Under Uncertainties

This paper proposes a two-stage microgrid (MG) scheduling approach that considers the dynamic MG formation and the coordinated optimization of active and reactive powers to enhance the distribution system resilience after blackouts. It aims to dispatch multiple devices operating in different timescales to sectionalize the distribution system into self-supplied MGs and supply the critical loads continuously with reliable power. In the hour-ahead operation stage, switches, capacitor banks and energy storage charging/discharging decisions are optimized based on hour-ahead interval predictions of renewable energy generations and load demands. In the intra-hour operation stage, the output of distributed energy resources and static Var compensators are redispatched to compensate the hour-ahead operation decisions after the uncertainty realization. Compared with the existing methods, the proposed MG scheduling method can restore more critical loads and sustain secure operation under random renewable energy generation and load demand. In addition, to address uncertainties, the MG scheduling problem is modeled as a robust optimization model, which is decomposed into a master problem and a subproblem by adopting the column-and-constraint generation algorithm. Furthermore, the subproblem is reformulated via the disjunctive programming method to reduce the computational complexity. The effectiveness of the proposed method is verified on the modified IEEE test systems.

Real-Time Schedule of Microgrid for Maximizing Battery Energy Storage Utilization

This paper proposes a real-time schedule model of a microgrid (MG) for maximizing battery energy storage (BES) utilization. To this end, a BES life model is linearized using piece-wise linearization and big-M method to assess the BES life loss (BLL) in a real-time manner. The cost-effective schedule model of the MG with multiple energy resources aims to maximize BES utilization while ensuring its sufficient lifespan. Corresponding to the optimization model, approximate dynamic programming (ADP) for maximizing BES utilization (ADP-MBU) in the real-time schedule is proposed to optimize the system under stochastic environments. In ADP-MBU, a new value function approximation method employing the BES cumulative life loss (BCLL) is developed to improve the optimality and applicability. The proposed ADP-MBU algorithm can achieve satisfactory approximate optimality while reflecting the variation of real-time BLL. Case studies validate the applicability of the proposed MG schedule model and the advantages of the proposed ADP-MBU algorithm.

A cost-effective solution to security constrained dispatch problem of Islanded microgrid using chaotic spotted Hyena optimization

ABSTRACT Power system network is growing day by day as per the increased demand due to technological development around the globe. The increased power demand needs optimized planning for all the sections of power system. Economic dispatch is one of the important areas in which new optimization techniques can add to the overall improvement in the power system planning. Recent technological improvement in microgrids results in economic and efficient operation considering both conventional and non-conventional methods of generation. This paper highlights the optimal scheduling requirement in the advanced power system control by introducing a new proposed metaheuristic hybrid approach, i.e., Chaotic Spotted Hyena Optimization (CSHO). The proposed approach improves and intensifies the conventional approach and the results obtained are compared with some well-known published approaches using standard data. The CSHO is implemented on an islanded microgrid having five distributed generators out of which three are conventional generators and two are renewable sources. Three objectives are tested on the microgrid-(1) Economic scheduling, (2) Environmental emission and (3) CEED for Microgrid and the results of proposed approach are tested in four scenarios and compared with the PSO, DE, SOS, GWO and WOA approaches. The result achieved from the proposed approach is remarkable over previous approaches and showing enhanced economic operation, which are verified and presented in different sections of paper.

Optimal energy scheduling of micro-grids considering the uncertainty of solar and wind renewable resources

Optimal energy scheduling of micro-grids considering the uncertainty of solar and wind renewable resources

Stochastic optimization for the scheduling of a grid-connected microgrid with a hybrid energy storage system considering multiple uncertainties

The deployment of a microgrid with renewable energy sources (RESs) generally considers two aspects i.e., the need for the energy storage and the presence of many uncertainties due to the intermittent nature of several RESs and the load demand variability. The latter justifies the use of stochastic optimization for the scheduling purpose. Therefore, this paper aims to investigate the optimal stochastic scheduling and evaluate the expected performance of a microgrid in grid-connected mode with a hybrid energy storage system consisting of a battery and a supercapacitor. The proposed optimization approach is formulated as a stochastic mixed-integer linear programming (MILP) problem and applied to a modified ORNL-DECC microgrid test system. The system uncertainties are associated with wind power, solar power, and load demand, which are represented by Gaussian distributed scenarios with standard deviations of 10%, 5%, and 3%, respectively. To investigate the effect of supercapacitor addition on the scheduling stochastic performance, the power rating of supercapacitor is varied between 0 and 20 kW. The analysis is performed based on the results of scheduling using the deterministic and the stochastic optimization schemes. In the deterministic scheme, the addition of supercapacitor is reviewed based on the achieved total cost. In the stochastic optimization scheme, we use the expected value of perfect information (EVPI) to observe the stochastic performance in addition to the expected total cost. The results are beneficial when assessing the advantage and disadvantage of supercapacitor deployment in a microgrid scheduling.

Multi-time-scale energy management for microgrid using expected-scenario-oriented stochastic optimization

The uncertainty of renewable distributed energy sources, load demand and electric vehicles bring great difficulties to the optimal scheduling of the grid-connected microgrid, this paper proposes a multi-time scale stochastic optimization model for microgrid with low operation cost and high reliability against these uncertainties. In the day-ahead scheduling stage, the Monte Carlo method is used to generate stochastic scenarios and simulate the uncertainty of the microgrid. The day-ahead stochastic optimization model is established considering the energy balance in the expected-scenario, the operation cost of distributed energy sources, the charging and discharging characteristics of electric vehicles, and the stable operation under multiple stochastic scenarios. Then the hourly robust scheduling decisions are obtained by solving the day-ahead stochastic optimization model to achieve the optimization objective of expected optimal and stochastic feasibility. In the intraday ultra-short-term scheduling stage, according to the updated minute forecast information, a rolling optimization strategy is carried out and the intraday optimal power allocation is obtained to minimize the operation cost in the prediction time domain. Even if the prediction error of the ultra-short-term forecast data is very small, it still adversely affects the stability of the system in the real-time operation stage. To eliminate the random fluctuations of renewable energy output and load demand in the real-time operation, the power deviation is smoothed through interacting with the power grid. Compared to the deterministic scheduling model, simulation results demonstrate that the proposed stochastic scheduling model and multi-time-scale optimization strategy are superior in minimizing the operation cost and reducing the negative impact of uncertainty on the economic operation.

Multi-Objective Optimal Source–Load Interaction Scheduling of Combined Heat and Power Microgrid Considering Stable Supply and Demand

With the development of smart grids, it has become possible to take demand-side resource utilization into account to improve the comprehensive benefits of combined heat and power microgrids (CHP-MGs). In order to improve the benign interaction between the source and the load of the system, the source side decouples the thermoelectric linkage through energy storage devices and improves the system multi-energy supply capacity by introducing various energy flow forms of energy devices. On the demand side, considering the elasticity of electric heating load and the diversity of heating mode, an integrated demand response (IDR) model is established, and a flexible IDR price compensation mechanism is introduced. On this basis, aiming at the optimal stability of supply and demand and the minimum operating cost of the system, a multi-objective optimal operation model of combined heat and power source–load interaction is constructed, taking into account the user satisfaction with energy consumption and the internal equipment load constraints of the system. Finally, an improved multi-objective optimization algorithm is used to solve the model. The analysis of the algorithm shows that the source–load interaction multi-objective optimal scheduling of the cogeneration microgrid considering the stability of supply and demand can effectively improve the stability of supply and demand and the economy of the system.

Robust microgrid energy trading and scheduling under budgeted uncertainty

The advent of smart grids came with several technological developments including new electricity market rules and regulation mechanisms. Microgrids can trade energy with the main grid to either sell its production surplus (from renewable energy sources) or buy an additional amount to support local consumers’ demand, which includes flexible loads, such as smart appliances and electric vehicles. In this scenario, smart control devices are important elements, executing real-time energy scheduling according to fluctuations in production and consumption. As we might expect, the main grid’s power generation and supply becomes more unscheduled and risky as energy trading quantities oscillate over time. This work studies a flexible energy contract subscription framework, coupled with a real-time command strategy, suited for energy scheduling of microgrids with uncertainty in both production and consumption. Our main contributions are a Robust Optimization model under budgeted uncertainty for contract subscription and a set of heuristic control strategies for the real-time energy scheduling. The robust model is capable of providing solutions for multi-period-ahead trading of energy, while minimizing the worst-case cost. We run an extensive computational case study on a real microgrid instance to confirm the efficacy of our solution approach.