This paper proposes a novel Stackelberg game approach for activating demand response (DR) program in a residential area aiming at addressing the mismatch between the demand and renewable energy generation. In this study, two major players, namely the aggregator as a leader and the consumers as followers, are considered. The aggregator, which owns a wind farm and also receives power from the independent system operator (ISO), strives to obtain the maximum matching between the consumers' demand and forecasted wind power by incentivizing consumers to adjust their load through offering a bonus to them. On the other hand, consumers change their load profiles for obtaining the highest amount of bonuses. Each consumer has two kinds of loads including critical loads, which must be maintained under any circumstances, and the flexible loads, e.g., heating, ventilation, and air conditioning (HVAC) system, which can be regulated for DR purposes. In order to consider the uncertainty associated with the wind generation and the demands, a scenario-based stochastic programming model has been adopted in this work. Results show the effectiveness of the Stackelberg game model used for interaction between the aggregator and consumers, and the best response that can be served to both of them.

Increasing the integration of distributed energy resources (DERs) inspired by environmental and governmental incentives, beside the introduction of multi-agent structure perspective have led to a paradigm shift of operational conditions in distribution systems. In this context, the traditional concept of fit and forget in the distribution grid management would not be efficient in the modern distribution systems and consequently new mechanisms should be developed in order to enable the distribution system operator (DSO) to efficiently manage the grid congestion caused by peak power output of DERs or load demands requests. In this paper, the Stackelberg game concept is employed to develop an incentive-based mechanism that facilitates the contribution of local flexible resources operated by independent agents in the congestion management of distribution grids. Therefore, the proposed bi-level formulation would result in congestion alleviation in the multi-agent system; while the objectives associated with DSO and independent agents are fulfilled. Additionally, the developed framework enables DSO to reconfigure the network to decrease the operational costs associated with the congestion alleviation procedure. Finally, the strong-duality concept is utilized in order to combine the twolevel problem into a one-level problem and the obtained model is implemented on IEEE-37 bus test system to investigate its effectiveness in congestion management in the distribution system.

This study suggests a model to include a solar power system or photovoltaic system (PV) in the control of frequency by taking into account a percentage of the PV power production for back-up reserve. This is done by investigating two scenarios: PV contribution in (1) initial primary frequency control and (2) entire primary frequency control. As explained in section three, 10% power of the PV modules which receive more than 400 w/m² irradiation is allocated for the power reserve. The power generation of photovoltaic systems depends largely on weather conditions which makes their output power associated with some degree of uncertainty. For this reason, in this paper, a PV system is considered along with conventional hydro and thermal units and they are modeled in MATLAB/Simulink (version 9.3, MathWorks, Natick, MA, USA) with the purpose of exploring the behavior of the intended method. In the next phase, for further studies, this system is extended to multi-area power systems including gas turbines. The results of the simulation demonstrated that the photovoltaic involvement in the control of frequency can successfully amend the frequency of the overall network. Not only it can decrease the overshoot and undershoot of the frequency response, it has the ability to improve the settling time as well, which helps the system reach the steady state easily and in shorter time. Specifically, the overshoot has reached nearly zero in both one area and two area systems and undershoot has declined up to 60% and 50% in the one area and two-area system, respectively. Considering settling time, while it had a negligible improvement in the one area system, it showed a remarkable enhancement in the two-area system, which improved from about 25 s to 6 s by using the proposed method.