Operational Optimization for Microgrid of Buildings with Distributed Solar Power and Battery

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power.Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small-scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self-organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time-of-use electricity market, price-based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm.

Section: Residential Vpp Modelingmentioning

confidence: 99%

Residential virtual power plant with photovoltaic output forecasting and demand response

Cui

Wang

Lin

et al. 2019

“…Previous works on the energy management problem in a hybrid electricity production system revolve around the following themes: ‐sizing and managing the storage system taking into account the electricity market (buying and selling tariffs), investment costs, consumption forecasts, and weather forecasts ; ‐optimizing the autonomy of the MG with respect to the main grid ; ‐involving the MG in the primary control of the main grid features (voltage control, frequency control) . …”

Section: Introductionmentioning

confidence: 99%

“…The objective function considered includes the cost of the cash flow and CO 2 emissions. However, all the papers mentioned in [4][5][6][7][8][9] have been limited to the optimization of the active power distribution on the various sources associated with the MG. Under these conditions the reactive power demanded by the load can be provided only by the main grid or by a reactive energy compensator.…”

Section: Introductionmentioning

confidence: 99%

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Managing a hybrid energy smart grid with a renewable energy source

Chakir

Ouadi

Giri

2019

The aim of this work is to develop tools for optimal power flow management control in a micro grid (MG). The latter consists of a wind turbine, energy storage system, two gas turbines (GTs), and the main grid. Unlike the traditional approach, which is limited to the distribution of active power, this paper models an electrical system to coordinate and optimize the flow of both active and reactive power using discrete controls. The proposed optimal power distribution strategy has two objectives. First, it aims at forecasting over a time horizon of 24 hours the optimal distribution of the active and reactive power required for each power source connected to the MG. The proposed management incorporates the forecasts of consumption, weather, and tariffs. Second, it aims at reducing the CO 2 emissions rate by optimizing both the operating point of the two GTs and the usage of the storage unit. The proposed optimization problem for the energy management system is solved using the Bellman algorithm through dynamic programming. Its effectiveness is illustrated with various simulations carried out in the Matlab environment. The supremacy of the proposed management algorithm is highlighted by comparing its performance with conventional (restricted) management. KEYWORDSBellman algorithm, distributed generation, dynamic programming, energy management system, hybrid system, optimal power flow DGs generally integrate certain renewable energy production systems. However, regarding the intermittent nature of these non-polluting sources, the introduction of an energy storage system is compulsory. Indeed, this will smooth the power produced and thus avoid grid stability problems. Moreover, this storage system is generally supported by other auxiliary sources to cope with the sharp fluctuations in electricity demand on one hand and renewable production on the other. In this context, the micro grid (MG) considered in this paper consists of:--

“…Microgrid is a small distribution system composed of distributed power supply, energy storage, energy conversion device, load, monitoring and protection devices . The extensive use of microgrid can provide users and power systems with many benefits.…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearization Based Distributed Model Predictive Control for Secondary Control of Islanded Microgrid

Zhou

Zheng

2018

In this paper, a distributed Model Predictive Control (DMPC) is proposed for the secondary voltage and frequency control of islanded microgrid, where each distributed generator (DG) is controlled by a Model Predictive Control (MPC) in the secondary control layer, individually. With considering the nonlinear dynamics of DG with primary control, input-output feedback schemes are developed for voltage and frequency control separately. Then, all MPCs use the local and neighboring nodes information to solve the optimization problem instead of communicating with a central controller. In this way, the control of the whole system is fully distributed, which allows for a plug-and-play. The convergence and stability analysis of the overall closed-loop system are provided. The simulation result shows the effectiveness of the proposed method.