Scheduling Model Of Microgrid Research Articles

Microgrid Formation and Real-Time Scheduling of Active Distribution Networks Considering Source-Load Stochasticity

The post-disruption microgrid (MG) formation and the subsequent scheduling are resilience-enhancing measures for active distribution networks (ADNs) against disastrous events. This paper proposes an integrated MG formation and scheduling solution, considering stochastic loads and distributed generators (DGs). Specifically, a first-stage MG formation model and a sec-ond-stage MG scheduling model are proposed to flexibly identify MG boundaries and restore as many critical loads as possible. The MG formation problem is formulated over multiple time steps to address time-varying generation outputs of intermittent DGs. Moreover, in the MG scheduling problem, the stochasticity of load demands and DG outputs is modeled as joint chance constraints (JCCs) to describe the possibility of security violations in the over-all system. An efficient approximation method is next developed to convexify the JCCs into solvable second-order cones. The interac-tion between the MG formation and scheduling by the model predictive control implementation achieves a responsive solution for enhancing the emergency support capability of ADNs by incor-porating power forecasts and real-time scheduling. The performance of the proposed method is verified in a modified real-world 136-node test feeder under catastrophic failures.

A multi-timescale energy scheduling model for microgrid embedded with differentiated electric vehicle charging management strategies

This paper presents a novelty energy management model embedded with differentiated charging management strategies for optimal scheduling of the microgrid (MG), including electric vehicles (EVs) and renewable energy sources (RES) in similar official buildings and industrial parks. The study firstly synthetically considers the objectives of multiple stakeholder entities in the MG and various uncertainties and employs a multi-timescale energy schedule model to satisfy each entity’s different objective and optimize the power procurement strategy. In the day-ahead stage, an enhanced variant multi-objective particle swarm optimization algorithm (EVMOPSO) is used to solve the multi-objective equilibrium optimization model, while in the real-time stage, a rolling correction model based on model predictive control (MPC) is introduced. Secondly, considering the significant diversity of driving behaviors of the EV users unique to the MG above, this study presents a set of differentiated charging management strategies for the first time in the multi-timescale energy management model. These strategies are based on credibility, which assigns different credibility levels to users by evaluating the degree of regularity of their driving behaviors to assign corresponding charging and price strategies. Simulation results verify the method’s practicality and validity, and the model’s robustness is also verified by sensitivity analysis.

Optimal schedule of 100% renewable energy microgrid considering demand response of EVs

With the breakthrough of key technologies of renewable energy, microgrid is gradually developing towards 100% renewable energy. However, aiming at the characteristics of renewable energy power such as randomness and intermittency, the microgrid needs to cope with the “double” fluctuations from the power source side and the load side, which will pay more costs for absorbing renewable energy. To implement “source-grid-load-storage” integration, this research establishes a hierarchical scheduling model for microgrid, which considers the demand response (DR) characteristics of electric vehicle (EV) users and is 100% powered by renewable energy. The model includes two layers, EV layer and renewable energy microgrid (REMG) layer. The first layer is EV layer. This layer formulates an appropriate charging price according to the user’s price response characteristics, and initially regulates the fluctuation of the original load while taking into account the customer satisfaction. The second layer is REMG layer. Based on the preliminarily optimized load, this layer adjusts the renewable energy rejection, the interactive power of main grid, and the output of pumped storage and battery storage under grid connection mode and island mode. The REMG layer can reduce system operating costs, while suppressing interactive power fluctuations and increasing the utilization rate of renewable energy. Simulation shows that the proposed optimal scheduling model can achieve win-win between EV and users.

Optimal regulation of microgrid considering electro-hydrogen coupling

Aiming at the problem of large access to renewable energy and insufficient regulation capacity of microgrid systems, this paper introduces electro-hydrogen coupling to improve the flexible regulation capacity of microgrid systems. A mathematical model for power regulation of microgrid including renewable energy equipment and electro-hydrogen coupling equipment is analyzed and established. Based on this, a flexibility margin model for the system is established. Taking the total profit and flexibility margin over the entire life cycle as optimization objectives, a daily optimal scheduling model for microgrid is established. In order to reflect the optimization and adjustment ability of electro-hydrogen coupling, this paper develops a variety of scenarios for comparison, and uses an improved particle optimization algorithm to solve the scheduling plans for different scenarios. The analysis of a numerical example shows that the regulation and optimization ability of the electro-hydrogen coupling is much higher than that of traditional energy storage.

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.

Enhanced-decision energy trading for island renewable microgrids considering large interruptible refrigeration load intra-hour switching

Large interruptible refrigeration load (LIRL) can be incorporated into the scheduling model of microgrid (MG) to optimize the operational cost. However, based on a classic 1-hour step, the LIRL is either turned on or off, resulting in unsatisfactory results. Although a smaller scheduling step can adjust the LIRI more finely, the computational burden will be increased simultaneously. To address the problem, this paper proposes a novel enhanced-decision energy trading strategy for renewable MGs. First, enhanced decisions are proposed, where on-state and off-state durations of LIRL in an hour instead of on or off state would be determined. On the basis, based on a 1-hour scheduling step, an enhanced-decision trading model is presented, where the energy operator maximizes the electricity sale profit, and load aggregators (LA) minimize the electricity usage cost. This optimization model is first converted into a classic mixed-integer quadratic programming (MIQP) problem, and then solved by a distributed method. Simulation studies indicate that the proposed energy trading strategy can reduce the electricity price fluctuation range by 0.43 yuan (20.6%), increase the social cost by 104 yuan (3.5%), and benefit the participators.

Robust Microgrid Scheduling Considering Unintentional Islanding Conditions

This work presents a novel microgrid scheduling model considering the stochastic unintentional islanding conditions as well as forecast errors of both renewable generation and loads. By optimizing the dispatch of distributed energy resources (DERs), utility grid, and demand, the proposed model is targeted to minimize total operating cost of the microgrid, including start-up and shut-down cost of distributed generators (DGs), operation and maintenance (O&M) cost of DGs, cost of buying/selling power from/to utility grid, degradation cost of energy storage systems (ESSs) and cost associated with load shedding. To capture the stochastic unintentional islanding conditions and conventional forecast errors of renewable generation and loads, a two-stage adaptive robust optimization is proposed to optimize the objective function in the worst case scenario of the modeled uncertainties. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The result obtained ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. The proposed model is validated with results of case studies on a microgrid consisting of various DGs and ESSs.

Frequency-Constrained Resilient Scheduling of Microgrid: A Distributionally Robust Approach

In order to prevent the potential frequency instability due to the high Power Electronics (PE) penetration under an unintentional islanding event, this paper presents a novel microgrid scheduling approach which includes the system frequency dynamics as well as the uncertainty associated with renewable energy resources and load. Synthetic Inertia (SI) control is applied to regulating the active power output of the Inverter-Based Generators (IBGs) to support the post-islanding frequency evaluation. The uncertainty associated with the noncritical load shedding is explicitly modeled based on the distributionally robust formulation to ensure resilient operation during islanding events. The resulted frequency constraints are derived analytically and reformulated into Second-Order Cone (SOC) form, which are further incorporated into the microgrid scheduling model, enabling optimal frequency services provision from the micorgrid perspective. With the SOC relaxation of the AC power flow constraints, the overall problem is constructed as a mixed-integer SOC Programming (MISOCP). The effectiveness of the proposed model is demonstrated based on modified IEEE 14-bus system.

Application of flexible ramping products with allocation rates in microgrid utilizing electric vehicles

Microgrids account for a relatively high proportion of renewable energy utilization. Therefore, an operation method that ensures flexible resources to cope with the variability and uncertainty in renewable energy is indispensable for microgrid operators (MGOs). Here, we focus on the necessity of an optimal generation scheduling model for MGOs by considering flexible resources, namely flexible ramping products (FRPs). We propose a microgrid scheduling model including a method of utilizing electric vehicles (EVs) as a flexible resources and an operation method to secure FRPs in microgrids by applying allocation indices. The states and available capacities of EVs during each period are estimated using a Markov chain. The net load scenarios, which are considered as the target for the supply–demand balancing in the proposed method, are generated considering the uncertainty in forecasting load and renewable energy. The effectiveness and merits of the proposed model are validated through experiments on a microgrid test system that allows transactions between utilities through the tie-line. The results confirm that the proposed optimal generation scheduling model affords a reduction in microgrid operating costs and enables the stabilization of variability.

Multi-Time Scale Economic Scheduling Method Based on Day-Ahead Robust Optimization and Intraday MPC Rolling Optimization for Microgrid

Due to the source and load prediction errors and uncertainties, the real operation state of microgrid may deviate significantly from the expected state, which leads to prevent the system from reaching its expected economic effects. In order to obtain the optimal economic effects for microgrid scheduling, an optimal microgrid scheduling model considered the demand responses is built in this paper firstly, and then a multi-time scale economic scheduling method based on day-ahead robust optimization and intraday model predictive control (MPC), is developed as well. Moreover, in the day-ahead stage, the long-time scale interval is set as 1 h and the robust optimization is used to address the low-frequency components in prediction errors and uncertainties. Meanwhile, the robust optimization enables to gain the day-head optimal economic scheduling plan for the microgrid and to keep the system operating effectively even when large-scale fluctuations happen. Furthermore, in the intraday stage, the short-time scale interval is set as 15 mins and MPC is adopted to track and correct the day-ahead economic scheduling plan, which enables to address the high-frequency components in prediction errors and uncertainties. Finally, simulation results demonstrate the feasibility of the proposed optimal microgrid scheduling model and the validity of the proposed multi-time scale economic scheduling method.

Real-time dispatch strategy of microgrid based on fuzzy control of energy storage

Microgrid (MG) is effective to absorb the distributed renewable energy source (RES). Considering the uncertainty of RES and load, this paper proposes a fuzzy rule of energy storage system (ESS) and gets its output by anti-fuzzification in the real-time dispatch stage after receiving the actual load and RES value. Based on the envelope principle and fuzzy control, a grid-connected MG scheduling model is established. Considering the ultra-short-term forecasted data at the beginning of an hour, a receding-horizon scheduling model which includes the constraints of ladder price, ESS, RES, etc. and aims at the lowest operating cost, is established to correct the day-ahead scheduling error as the basic value of real-time scheduling at subsequent times. Taking a MG in Shaanxi Province as an example, the proposed strategy ensures the rapid and economic operation of MG.

A microgrid scheduling method based on Evolutionary Game

In order to solve constrained scheduling problem in microgrid. A micrgrid scheduling method based on evolutionary game is proposed in this paper. Further more, a renewable energy and traditional energy generators group with different cost functions was studied as an example. The results show that evolutionary game method proposed in this paper has higher accuracy and stability than the existing artificial fish swarm optimization algorithm (AFS). In the scheduling model of microgrid, the optimized cost of power supply has been reduced by 2%.

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.

Robust Microgrid Scheduling With Resiliency Considerations

A new microgrid scheduling model with resiliency guaranteed under the risk of both utility failure and prevailing uncertainties of renewable generation and load is proposed in this article. The proposed model minimizes the overall operating cost of the microgrid by efficiently coordinating the power supply from local distributed energy resources and the main grid. The resiliency is ensured by maintaining certain amount of flexibility in local distributed energy resources, which can be quickly deployed to keep the power supply uninterrupted whenever the utility grid suddenly goes down. In addition, the uncertainties of renewable generation and load are captured with the proposed two-stage robust optimization model. By solving the proposed optimization, the solution not only guarantees the resiliency of the microgrid by supporting possible islanding incidents without load interruption, but also ensures robustness against the randomness of renewable generation and load. Results of case studies on a typical microgrid demonstrate the effectiveness of the presented robust microgrid scheduling model.

Day-Ahead Optimization Scheduling for Islanded Microgrid Considering Units Frequency Regulation Characteristics and Demand Response

With the increase of distributed generation penetration, the economic operation of microgrid has been fully developed. For islanded microgrid, due to the lack of large grid support and the limited capacity of the distributed generation (DG), the energy supply of microgrid may not meet the need of users in real time. Therefore, it is of great significance to solve the problem of insufficient power supply of islanded microgrid and improve the economy of its operation. Firstly, this paper analyses the frequency characteristics of DG, and establishes power models oriented to the frequency regulation characteristics of microgrid and introduces the mathematical model of DG. Then, an islanded microgrid day-ahead optimization scheduling model considering frequency regulation characteristics of units and demand response (DR) is proposed. The frequency characteristics of controllable units and DR are considered in the model, and different strategies are selected to analyze their impact on microgrid economy. The example shows that the optimization scheduling model of microgrid established in this paper can reduce the power supply pressure during peak load periods, alleviate the problem of insufficient power supply, and effectively improve the overall benefit of microgrid, which verifies the rationality of the model.

Adaptive robust optimization framework for day-ahead microgrid scheduling

This paper proposes a framework to address day-ahead scheduling of a microgrid (MG) with wind, solar and micro-turbine distributed generators as well as batteries, considering the associated uncertainties and AC power flow constraints. The problem uncertainties are wind and solar generations, demand, and grid electricity price. The uncertainties are modelled using bounded intervals within adaptive robust optimization method. Unlike previous robust models presented for MG scheduling, our proposed adaptive robust model optimizes both wait-and-see and here-and-now decisions simultaneously which leads to obtaining a better operating point. In order to make wait-and-see and here-and-now decisions and immunize the model against worst case realizations, the proposed MG scheduling model has been formulated as a tri-level optimization framework. In the first level, here-and-now commitment decisions are made (these are decisions made before realization of uncertainties). In the second stage, the worst case realization of the uncertain parameters is determined. In the third level, the wait-and-see recourse decisions are made (these are decisions made after realization of uncertainties). Since there are before uncertainty-realization decisions, worst uncertainty-realization determination, and after uncertainty-realization decisions, the proposed model becomes a tri-level optimization framework. The proposed adaptive robust tri-level MG scheduling model, the proposed solution approach including AdptRob and ConvAC problems, and the proposed out-of-sample analysis are the main contributions of this paper. A convexified AC power flow model is used in the proposed framework for modeling the distribution network. The 69-bus distribution test system is used to test the proposed adaptive robust model as well as the proposed solution approach which converges very fast. Moreover, the results of the out-of-sample analysis shows that the proposed adaptive robust model results in lower expected cost and lower cost variance.

Flexible Scheduling of Microgrid With Uncertainties Considering Expectation and Robustness

With increasing renewable distributed energy resources integrated into a microgrid (MG), it is difficult to realize its optimal operation due to various uncertainties, e.g., power outputs of wind turbines and photovoltaic cells. This paper presents a flexible scheduling model for the optimal operation of an MG in which the expectation of operational cost and the robustness of scheduling solutions are considered, simultaneously. To balance the tradeoff between minimizing the expectation of operational cost and enhancing robustness, the proposed flexible scheduling model of MG is formulated as a multiobjective optimization problem by introducing a robustness preference parameter. In this way, the relationship between the expected operational cost and the robustness of scheduling solution is investigated, and the fuzzy decision making method is used for selecting a suitable solution. Finally, a modified MG is used for case study, and simulation results verify the applicability and effectiveness of the proposed model.

Market-Based Versus Price-Based Microgrid Optimal Scheduling

An optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with a Distribution Market Operator (DMO) is proposed in this paper. The DMO administers the established electricity market in the distribution level, sets electricity prices, determines the amount of the power exchange among market participants, and interacts with the Independent System Operator (ISO). Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model and further investigate merits over a price-based scheme.

Economic scheduling model of microgrid considering the lifetime of batteries

Because of the uncertainty of renewable energy generation and load, batteries as energy storage devices play an important role in ensuring the safety and reliability of microgrid. To take full advantage of the batteries, the battery lifetime characteristics are analysed, and a weighted Wh throughput method is proposed to estimate the battery lifetime. To improve the economy of microgrid, an economic scheduling model of microgrid in grid-connected mode is established with the consideration of battery lifetime. For fast and efficiently solving the model, a technique is developed to convert the optimisation problem into a mixed integer linear programming problem and a mixed integer linear programming algorithm is applied to solve it. The proposed method has been validated on a microgrid, which consists of a wind turbine, a photovoltaic system, a micro turbine, a fuel cell, a diesel engine and a battery energy storage system (BESS). The simulation results show that the BESS is managed rationally and the total operation cost of microgrid over scheduling period is decreased by applying the proposed scheduling method.

Risk-minimizing stochastic self-scheduling model for microgrid in day-ahead electricity market

Summary This paper presents a new stochastic operation scheduling model for microgrid in day-ahead electricity market considering the forecast uncertainties of wind generations, microgrid's load, and locational marginal price of the point of common coupling, as well as the uncertainties pertaining to availability of generation units of the microgrid. The financial risk caused by these uncertainties is modeled by conditional value-at-risk criterion. The proposed approach is formulated as a risk-minimizing two-stage stochastic model based on mixed-integer linear programming framework. This model minimizes the expected scheduling cost together with the cost of financial risk. The impacts of different uncertainty sources on the model's results in both grid-connected and islanded operation modes are extensively studied. Additionally, higher effectiveness of the proposed stochastic model compared with common deterministic approach, regarding total operation cost and convergence behavior, is illustrated through an out-of-sample analysis.