As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. VPPs can participate in the day-ahead (DA) market and therefore require a bidding method that maximizes profits. It is also important to minimize the variability of VPP output during intra-day (ID) operations. This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. Hourly bids are determined based on VPP revenue in the DA market bidding step, and the schedule of DERs is revised in the ID operation to minimize the impact of forecasting errors and maximize the incentives, thus reducing the variability and uncertainty of VPP output. The simulation results verify the effectiveness of the proposed methods through a comparison of daily revenue.
The effective use of energy is emphasized in a smart home environment because of concerns regarding growing energy demands. To save energy in a smart home, detailed energy consumption information at the appliance level needs to be monitored and delivered to consumers. Unlike distributed direct sensing, non-intrusive load monitoring (NILM) is receiving increased attention as a low-cost, low-complexity load motoring technique. This paper presents an electrical event identification technique for monitoring home appliance load using appliance load signatures. The load signature acquisition system is also introduced in the paper.
Periodic preventive maintenance of generators is required to maintain the reliable operation of a power system. However, generators under maintenance cannot supply electrical energy to the power system; therefore, it is important to determine an optimal generator maintenance schedule to facilitate efficient supply. The schedule should consider various constraints of the reliability-based demand response program, power system security, and restoration. Determining the optimal generator maintenance schedule is generally formulated as a non-linear optimization problem, which leads to difficulties in obtaining the optimal solution when the various power system constraints are considered. This study proposes a generator maintenance scheduling (GMS) method using transformation of mixed integer polynomial programming in a power system incorporating demand response. The GMS method is designed to deal with various system requirements and characteristics of demand response within a power system. A case study is conducted using data from the Korean power system to demonstrate the effectiveness of the proposed method for determining the optimal maintenance schedule. The results show that the proposed GMS method can be used to facilitate the efficient and reliable operation of a power system, by considering the applicable system constraints.
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