Demand side management for Wind Power Integration in microgrid using dynamic potential game theory

Abstract

We propose a novel demand side management method to tackle the intermittency in wind power generation. Our focus is on an isolated microgrid with one wind turbine, one fastresponding conventional generator, and several users. Users act as independent decision makers in shaping their own load profiles. Using dynamic potential game theory, we analyze and coordinate the interactions among users to efficiently utilize the available renewable and conventional energy resources to minimize the total energy cost in the system. We further model the intertemporal variations of the available wind power as a Markov chain based on field data. Using techniques from dynamic potential game theory, we first derive closed-form expressions for the best responses for the users that participate in demand side management. Then, we investigate the efficiency of the constructed game model at the equilibrium. Finally, the system performance is assessed using computer simulation. In particular, our proposed scheme saves 38% generation cost compared with the case without demand side management.

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

“…EV driving patterns which directly determine the parameters of the aggregate battery storage should gain more attention; 2) the variability of aggregate battery caused by individual EV's driving pattern and the battery cycle life reduction caused by V2G implementation are not considered in [20][21][22][23][24]. There are also few works on a proper framework to integrate the coordinated EV charging/discharging allocation into the microgrid energy management; 3) how to restrain the impact of multiple neighboring microgrids on the main grid is another focus of this work, which, however, is not considered by most of related works [10][11][12][13][14][15][16].…”

“…The energy change of the aggregate battery storage caused by EV arrival and departure is shown in (12). Equation (13) defines the lower bound of remaining battery capacity, which must meet the driving demand and the battery security constraint.…”

“…Among these strategies, genetic algorithm [10] and lagrangian relaxation [11] are proposed to solve the problem of dispatchable generator's output scheduling and the battery charging planning with distributed renewable energy sources from a perspective of centralized control. To avoid the problem that the autonomous behavior of each consumer and the interaction between the consumers and the generators are always ignored in centralized control strategies, game theories [12][13][14] and pricing mechanisms [15,16] are exploited to facilitate the microgrid operation. However, all these methods focus on the cost minimization or benefit maximization within a microgrid.…”

“…Many strategies are proposed to reduce the total cost or minimize the power losses within a microgrid [10][11][12][13][14][15][16][17]. Most of them focused on individual microgrid optimization without considering the possible impact on the main grid.…”

Demand Side Management for Stand-Alone Hybrid Power Systems Based on Load Identification

“…For instance, since solar generation is concentrated in periods, to hold the demand together matching such periods instead of flattening the demand curve can be advisable (note that it is an opposite strategy to the expected for the normal grid). As far as wind generation is concerned, the localized and stochastic nature of wind also requires the application of accurate load management and control strategies able to cope with intermittency and fluctuations that can cause power and system degradation and failures [15]. An introduction to stand-alone microgrids, HPS and power management strategies (PMS) can be seen in [16].…”