Stochastic Predictive Control of Multi-Microgrid Systems

Abstract

In this paper, integrated operation management of cooperative microgrids is formulated in the framework of stochastic predictive control. In the proposed scheme, a joint probabilistic constraint on the microgrids power exchange with the main grid couples operation of individual microgrids. In order to tackle the coupling constraint, a cooperative energy management strategy is proposed in which based on the statistical characteristics of uncertain parameters, the deterministic counterpart of the problem is derived and an efficient solution strategy is achieved. The proposed strategy is evaluated for an illustrative test case including two microgrids based on modified CIGRE benchmark. Moreover, statistical analysis is conducted to evaluate robustness characteristics of the solution strategy.

Robust Energy Management and Economic Analysis of Microgrids Considering Different Battery Characteristics

“…Different BSS technologies with several initial charge possibilities and DOD values are taken into account in this work, in addition to the variation of the energy market price and electrical loads uncertainty. The results obtained show that the total operating cost of the MG can be reduced considerably when a BSS is integrated into the MG. We outlined the main findings of this study as follows: (1) the total cost per day of the BSS through the lifetime of the project decreases when the value of DOD increases; however, this may not lead to a reduction of the total operating cost because of the other factors that affect the operation, such as the charging/discharging schedule of the BSS, output power of each DG, and power imported from or exported to the main grid; (2) among the different BSS technologies, the impact of NaS batteries in the reduction of total operating cost of MG is clearer than the other BSS batteries due to their high efficiency and long life time, which resulted in a reduction in the total operating cost from $1861.57 per day (with no BSS) to $1458.8 per day, a total daily saving of 21.6%; (3) although lead acid batteries have a lower capital cost compared with the other batteries, this results in a high total operating cost due to their low efficiency and short life time; (4) the total cost saving depends substantially on the lifetime and efficiency of the battery; (5) the initial charge of the BSS affects the charging/discharging schedule of the BSS, and consequently changes the output power of each DG, and the power imported from or exported to the main grid to achieve the minimum objective function; (6) the variation of the energy market price is modeled by the stochastic optimization to obtain the actual results, and the total operating cost of the MG obtained was $1406.7, which is 3.6% lower than that achieved in the deterministic case; (7) it was also concluded that deterministic studies that ignore the uncertainty of parameters result in conservative results that usually lead to a noticeable overestimation of the costs; (8) a robust optimization technique is employed to obtain the most economic hourly operation of the studied MG and protect the MGCC decisions against high costs, in which the IGDT is used to model the risk of load uncertainty and then to provide more robustness to the MGCC decisions by utilizing the available load forecast in a method where a maximum suitable operation cost of the MG can always be guaranteed using the best results obtained in the deterministic case; (9) the performance of GAMS in all cases was better than the performance of other optimization techniques such as SOS, KHS, FS, and CS. Finally, a factor that was beyond the framework of the study, and will be included in future studies, is the techno-environmental concern to minimize the emissions of the MG along with the total operating cost, taking into account various uncertain parameters and high penetration of renewables.…”

“…Also, the creation of economic incentives and increased usage of consumer-interactive renewable energy sources (RESs) remain the leading solutions to transit away from traditional high-carbon energy sources [1]- [4]. In this regard, a microgrid (MG) is projected as a localized consumer-interactive distribution network construction within the smart grid community, to achieve a low-carbon society with reduced greenhouse gas emissions, while taking into account the local-generation properties, variability in the generation inputs and economic aspects [5]- [7]. MGs comprise different types of distributed generation (DG) units, energy storage systems, and electrical loads, and can operate either with the grid (grid-connected mode) or without the grid (islanded mode) [8], [9] to create an efficient and more economical system with enhanced power quality and reliability performance levels, increased energy efficiency, and reduced environmental pollution [10], [11].…”

“…The objective function (OF) is to minimize the total operating cost of the MG, f (x). Mathematically, f (x) is divided into three parts as given in (7), in which the first part (C t ) expresses the operation cost of the main grid ($/kWh), generation costs of DGs ($/kWh), and start-up and shut-down costs of the MT and FC, the second part (C OM ) expresses the operation and maintenance cost of the DGs, and the third part (TIC BSS ) expresses the total cost per day of the BSS through the lifetime of the project.…”

Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

“…The model consists of an incentive mechanism derived from the Stackelberg game, which maximizes the payoff of each MG while neglecting arbitration agent. The author of [21] propose a cooperative EM strategy for integrated operations of MMG. This strategy is a stochastic predictive control that solves the coupling constraints problem.…”

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

“…The MPC-based optimization approach has over-time, drawn the consideration of the power system network attributable to a few focal points over the Metaheuristic and Heuristic control techniques. The MPC-based control scheme's advantages over other control schemes are, and are not limited to the following criteria [11]. It focuses on the future behavior and predictions of the system and is therefore extremely appealing to systems that are inherently dependent on forecasting energy demand and the production of renewable energy, and offers a feedback mechanism that makes the system more sensitive to uncertainty and disturbance [1], [12].…”