Control Strategy for a Small-Scale Microgrid Based on Battery Energy Storage System-Virtual Synchronous Generator (bess-Vsg)

As one of widely deployed renewable energy resources, PV power is playing a very important role in microgrids today. It has advantages such as making the best of natural solar energy and being friendly to our environment. In this thesis, solar PV based microgrid is studied using modeling and simulation. Microgrid can run in either grid-connected-mode or islanded-mode. However, there are also some disadvantages for solar power. For solar panel, its output is influenced by weather conditions such as illumination intensity and temperature. In addition, during the control process of grid-connected mode, it is hard to guarantee its output power at the maximum power point all the time. In this thesis, the Maximum Power Point Tracking (MPPT) control for Solar PV energy is used. Besides, the frequency control is also a very important issue for guaranteeing the quality of the electricity in the microgrid. By using an effective way of BESS-VSG, which means Battery Energy Storage System-Virtual Synchronous Generator, the frequency can be controlled to the nominal value faster and more smoothly when there is a fluctuation in the PV power generation and/or load change, leading to higher stability and robustness. This thesis focuses on the modeling and control of the PV and BESS-VSG system, and the proposed modeling and control method are verified in MATLAB/Simulink

Load frequency control of power system based on improved AFSA-PSO event-triggering scheme

Aiming at the impact of redundant information transmission on network resource utilization in current power systems, an improved event-triggered scheme based on particle swarm optimization and artificial fish swarm algorithm for power system load frequency control (LFC) with renewable energy is proposed. First of all, to keep the stability and security of power systems with renewable energy, the load frequency control scheme is investigated in this paper. Then, to relieve the communication burden and increase network utilization, an improved event-triggered scheme based on the particle swarm algorithm and artificial fish swarm algorithm is explored for the power system load frequency control. Then, by utilizing improved Lyapunov functional and the linear matrix inequality method, sufficient condition for the H∞ stability of the load frequency control system is established. Finally, a two-area load frequency control system and IEEE-39 node simulation models are constructed to verify the effectiveness and applicability of the proposed method

Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Environmental aspects have been highlighted in architecting future energy systems where sustainable development plays a key role. Sustainable development in the energy sector has been defined as a potential solution for enhancing the energy system to meet the future energy requirements without interfering with the environment and energy provision. In this regard, studying the cross-impact of various energy vectors and releasing their inherent operational flexibility is main topic. Thecoordinationofvariousenergyvectorsundertheconceptofmulti-energysystem (MES)hasintroducednewsourcesofoperationalflexibilitytothesystemmanagers. MES considers both interactions among the energy carriers and the decision makers in an interdependent environment to increase the total efficiency of the system and reveal the hidden synergy among energy carriers. This thesis addresses a framework for modeling multi-energy players (MEP) that are coupled based on price signal in multi-energy system (MES) in a competitive environment. MEP is defined as an energy player who can consume or deliver more than one type of energy carriers. At first, the course of evolution for the energy system from today independent energy systems to a fully integrated MES is presented and the fractal structure is described for of MES architecture. Moreover, the operational behavior of plug-in electric vehicles’ parking lots and multi-energy demands’ external dependency are modeled in MES framework to enhance the operational flexibility of local energy systems (LES). In the fractal environment, there exist conflicts among MEPs’ decision making in a same layer and other layers. Realizing the inherent flexibility of MES is the main key for modeling the conflicts in this multi-layer structure. The conflict between two layers of players is modeled based on a bi-level approach. In this problem, the first level is the MEP level where the player maximizes its profit while satisfying LES energy exchange. The LES’s exchange energy price is the output of this level. In the lower level, the LESs schedule their energy balance, based on the upper level input price signal. The problem is transformed into a mathematical program with equilibrium constraint (MPEC) through duality theory. In the next step, high penetration of multi-energy players in the electricity market is modeled and their impacts on electricity market equilibrium are investigated. In such a model, MEP participates in the local energy and wholesale electricity markets simultaneously. MEP and the other players’ objectives in these two markets conflict with each other. Each of these conflicts is modeled based on bi-level programming. The bi-level problems are transformed into a single level mixed-integer linear problem by applying duality theory

Low-carbon economic scheduling of virtual power plant considering carbon emission flow and demand response

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source-load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model

A novel framework for photovoltaic energy optimization based on supply–demand constraints

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions

Islanding Detection and Power Quality Analysis in Microgrid

芝浦工業大学2019年

Asynchronous Distributed Power Control of Multimicrogrid Systems

Asynchrony widely exists in microgrids (MGs), such as nonidentical sampling rates and communication delays, which challenges the MG control. This article addresses the asynchronous distributed power control problem of hybrid microgrids, considering different kinds of asynchrony, such as nonidentical sampling rates, and random time delays. To this end, we first formulate the economic dispatch problem of MGs, and devise a synchronous algorithm. Then, we analyze the impact of asynchrony, and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point iteration problem with a nonexpansive operator, leading to a convergence proof. We further provide an upper bound estimation of the time delay. Moreover, the real-time implementation of the proposed algorithm in both ac and dc MGs is introduced. By measuring the frequency/voltage, the controller is simplified by reducing one order, and adapting to the fast varying load demand. Finally, simulations on a benchmark MG, and experiments are utilized to verify the effectiveness, and advantages of the proposed algorithm