The number of real-time supervisory control and data acquisition (SCADA) measurements in power distribution systems is scarce. This limits the reliability of state estimation (SE) results for distribution systems. Therefore, some studies seek to enhance the observability and SE accuracy of distribution systems by incorporating advanced metering infrastructure (AMI) data with the SCADA measurements. However, the hourly updated AMI data may be too coarse to capture system changes, especially in the presence of intermittent renewable energy sources. This issue is addressed by proposing a hybrid SE framework integrating a data-driven estimator and a model-based estimator. To be specific, the data-driven estimator combined with a topology identification method is presented to solve the DSSE problem between AMI scans, and the model-based estimator is employed to ensure robust estimation results against gross errors at a lower time scale. The proposed hybrid SE switches from the data-driven estimator to the model-based estimator once the AMI data is updated. Such a solution allows for capturing system changes at different time scales and improving the real-time and reliability of distribution system state estimation. Simulations are conducted on a sample distribution system to illustrate the characteristics of the proposed hybrid SE.
Integrated energy systems (IESs) provide a synergistic supply of electricity and heat, which offers a new approach for district heating. However, the operational risk of IESs increases continuously as the extent of renewable energy source (RES) penetration continues to increase. This condition negatively impacts the benefits of IESs to the participants in the integrated energy market (IEM) and increases the costs of the system. This paper addresses these issues by proposing a two-stage stochastic programming model that considers RES penetration for IESs operated independently of electricity grids (i.e., islanded IESs). Then, a two-stage clearing model of the IEM is established, and a market settlement process is designed. The simulation results demonstrate that the two-stage clearing model of the IEM can effectively alleviate the operational risk associated with high RES penetration by allowing market participants to make timely adjustments corresponding to their interests according to changes in RES generation. INDEX TERMS Real-time market, integrated electricity and heat system, integrated energy market, market clearing, market settlement, two-stage stochastic programming.
The establishment and development of an integrated energy market (IEM) contributes to the equitable distribution of electrical and thermal energy production resources. However, the application of conventional locational marginal price theory generally fails to promote the declaration of truthful marginal costs by market participants in the process of clearing and settlement of the IEM, which detracts from market fairness and may reduce market efficiency. Simultaneously, the continuous expansion in the scale of renewable energy sources (RESs) threatens the safe and stable operation of electrical power systems. Accordingly, the present study seeks to improve the efficiency of the IEM under large-scale RES penetration, and promote the truthful declarations of market participants by applying a Vickrey-Clarke-Groves (VCG) auction scheme to the IEM, and establishes a two-stage IEM model that promotes compatibility between the incentives of market participants to enhance market fairness. The present study also addresses the imbalance between market revenue and expenditure typically produced by the VCG auction scheme by designing an ex-post payment redistribution mechanism to ensure the equitable cost recovery of all market participants. Simulation results demonstrate that the application of the proposed VCG auction system to the IEM ensures maximum efficiency, cost recovery, and incentive compatibility as dominant strategies, and helps to integrate large-scale RES penetration with the IEM. INDEX TERMS Regional integrated energy system, integrated energy market, incentive compatibility, VCG auction, renewable energy system. NUMENCLATURE A. INDICES FIGURE 5. Periodic demand utility, system cost, and social welfare of Case 1A. FIGURE 6. Periodic demand utility, system cost, and social welfare of Case 1B. FIGURE 7. Periodic demand utility, system cost, and social welfare of Case 1C.
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