Hepeng Li

Analysis of multi-agent-based adaptive droop-controlled AC microgrids with PSCAD: Modeling and simulation

A microgrid (MG) with integrated renewable energy resources can benefit both utility companies and customers. As a result, they are attracting a great deal of attention. The control of a MG is very important for the stable operation of a MG. The droop-control method is popular since it avoids circulating currents among the converters without using any critical communication between them. Traditional droop control methods have the drawback of an inherent trade-off between power sharing and voltage and frequency regulation. An adaptive droop control method is proposed, which can operate in both the island mode and the grid-connected mode. It can also ensure smooth switching between these two modes. Furthermore, the voltage and frequency of a MG can be restored by using the proposed droop controller. Meanwhile, the active power can be dispatched appropriately in both operating modes based on the capacity or running cost of the Distributed Generators (DGs). The global information (such as the average voltage and output active power of the MG and so on) required by the proposed droop control method to restore the voltage and frequency deviations can be acquired distributedly based on the Multi Agent System (MAS). Simulation studies in PSCAD demonstrate the effectiveness of the proposed control method.

A stochastic programming strategy in microgrid cyber physical energy system for energy optimal operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.

A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid

Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user’s comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user’s comfort level simultaneously. Simulation results indicate that the algorithm can reduce user’s electricity cost significantly, ensure user’s comfort level, and take a tradeoff between the cost and comfort level conveniently.

MAS based distributed automatic generation control for cyber-physical microgrid system

The microgrid is a typical cyber-physical microgrid system (CPMS). The physical unconventional distributed generators (DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components, such as the embedded computer and communication network, are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid, the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information (such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system (MAS). Based on the global information from cyber components of CPMS, automatic generation control (AGC) and automatic voltage control (AVC) are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.

Day-ahead hourly photovoltaic generation forecasting using extreme learning machine

The photovoltaic (PV) generation systems as environmentally friendly renewable energy sources are increasing. However, the power generation of solar has high uncertainty and intermittency and brings significant challenges to power system operators. The accurate forecasting of photovoltaic (PV) power production is good for both the grid and individual smart homes. In this paper, we propose a novel weather-based photovoltaic generation forecasting approach using extreme learning machine (ELM) for 1-day ahead hourly forecasting of PV power output. In the proposed approach, the weather conditions are divided into three types which are sunny day, cloudy day, and rainy day and training the PV power output forecasting models separately for those three weather types. In this paper, we take the PV output history data from the PV experiment system located in Shanghai for case study. The forecasting results show that the proposed model outperform the BP neural networks model in all three weather types.

A genetic algorithm-based hybrid optimization approach for microgrid energy management

This paper proposes a novel Meta-heuristic based hybrid optimization method for Microgrid energy management system. First, microgrid energy management problem is modeled as a mixed integer nonlinear programming with the consideration of quadratic fuel cost of distributed generators and their startup/shut-down states. In order to obtain a favorable solution, a hybrid solution procedure combined quadratic programming and genetic algorithm is proposed to solve the problem. Then, the proposed method is verified via numerical simulation. Through the comparison of optimization result with IBM ILOG CPLEX Optimizer, simulation shows that the proposed algorithm has the advantage of finding better scheduling solution which leads to less operating cost.

Optimal home energy management integrating random PV and appliances based on stochastic programming

This paper presents an detailed study about the development of an integrative DR policy for the optimal home energy management system under stochastic environment. In this study, home appliances are classified into three categories and detailed modeling of all kinds of home appliances is given. Then, the optimal HEMS problem is formulated as a stochastic programming model considering the uncertainties of PV production and critical loads to minimize a customers electricity cost. Monte Carlo simulation method is used to decompose the problem into a mixed integer linear programming problem. Finally, the proposed stochastic programming model is verified through numerical simulation. The simulation results show that the proposed stochastic DR model can reduce the effect of the uncertainties in residential environment on the electricity cost and obtain a better DR policy than the conventional deterministic model.

Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties

Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.