Microgrids energy management systems: A critical review on methods, solutions, and prospects

Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings

Evolution of microgrids with converter-interfaced generations: Challenges and opportunities

This paper presents a literature review on the selected applications of renewable resource and power forecasting models to facilitate the optimal integration of renewable energy (RE) in power systems. This review is drafted on the basis of the selected high quality research publications from the past decade. Although the development of forecast models for RE generation, i.e., wind and solar energy, is a well-researched area, however, the performance of these models is usually evaluated using statistical error metrics. With regard to application, determining the optimality of accurate forecasts in terms of system economics and major planning aspects is an emerging phenomenon, that chalks out the main subject area of this survey. Specifically, the application domains include: 1) optimal power system dispatch (unit commitment, generation scheduling, economic dispatch), 2) optimal sizing of energy storage system, 3) energy market policies and profit maximization of market participants, 4) reliability assessment, and 5) optimal reserve size determination in power systems. The application-oriented review on these vital areas can be used by the power sector for familiarization with the recent trends and for analyzing the impact of forecasting improvement on optimal power system design and operation.

A review on the selected applications of forecasting models in renewable power systems

Thermo-economic optimization of a combined cooling, heating and power system based on small-scale compressed air energy storage

Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultra-short-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply.

An Online Optimal Dispatch Schedule for CCHP Microgrids Based on Model Predictive Control

Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.

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A robust optimization method for energy management of CCHP microgrid

Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultrashort-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply.

An online optimal dispatch schedule for CCHP microgrids based on model predictive control

The invention discloses a model predication control based cooling heating and power generation type micro-grid operation method. The method comprises the steps of building a predication model to predicate the wind power, photovoltaic power and cooling heating power generation load power data within a control time domain in the future; solving a rolling optimizing model at every moment according to the latest predication result and the real-time operation state of each device; calculating the force output of each device at the following time quantum; re-calculating the rolling optimizing model at the next time according to the latest predication result, monitoring the actual wind power value, photovoltaic value, cooling load value, heating load value and power generation load value on real time; updating the historic data; solving and feeding back to correct the model at every 5 minutes to obtain the adjustment of each device; distributing to each device for adjusting until reaching the next rolling optimizing time. With the adoption of the method, the influence of inaccurate predication on the optimal operation of the system can be removed well, thus the system operation risk is reduced, and the system operation stability is improved; meanwhile, the economy of the operation of a cooling heating and power generation type micro-grid can be increased.

Model predication control based cooling heating and power generation type micro-grid operation method

A two-stage energy management strategy for CCHP microgrid is proposed in this paper. The first stage, named day-ahead scheduling stage (DASS), is an economic dispatch model to minimize the operating cost. This stage provides the operation plans including commitment or output power of each unit, which is solved by a revised particle swarm optimization. The second stage, called real-time adjusting stage (RTAS), deals with the fluctuations of power by adjusting the output power of the central air conditioning considering housing characteristics, and the batteries and other controllable units based on heuristic rules. RTAS is utilized to maintain the exchange power between the main grids and the CCHP microgrid constant. A typical CCHP microgrid is simulated to demonstrate the performance and effectiveness of the proposed energy management strategy.

Wang Zhihe

Papers

An Online Optimal Dispatch Schedule for CCHP Microgrids Based on Model Predictive Control

A robust optimization method for energy management of CCHP microgrid

An online optimal dispatch schedule for CCHP microgrids based on model predictive control

Model predication control based cooling heating and power generation type micro-grid operation method

A Two-Stage Energy Management Strategy for CCHP Microgrid considering house characteristics