Bi-Level Programming for Integrating Flexible Demand in Combined Smart Energy System

Abstract

In this paper, a gas-electricity-heat integrated energy system with smart buildings is described. The special focus is flexible energy demand including electricity and heat integrated into the smart buildings, where customers have multiple options to satisfy their energy demand. Considering energy prices in the market, the aggregator is introduced to manage these smart buildings in the most economical way. This paper proposes a bi-level programming approach for the integration of flexible demand in the combined smart energy system (CoSES). The integrated system operator (ISO) aims at maximizing social welfare according to the operation strategy of the aggregator, which minimizes the energy purchase cost of downstream demand. Then, the linearization method and Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level optimization problem into an easily handled singlelevel problem. The proposed approach is illustrated in a modified test system.

Optimal Management of Flexible Resources in Multi-Energy Systems

“…Here the aggregator acts as an intermediate agency between public utilities and end-users [184]. On the one hand, the aggregator needs to reasonably arrange and control the buildings' energy facilities to meet the daily electricity and heat demands of users.…”

“…4.6 shows the input parameters of the model, including the hourly wind power, hourly solar power, electric load, gas load and heat load profiles. It should be noted that CFP and GFCHP units have minimum output constraints during the short-term scheduling to prevent the high cost of unit startup/shutdown [184]. According to the profiles of energy loads and renewable outputs in Fig.…”

“…5.10 shows energy pricing in the market at the optimal strategy of the MES. Here, the large units such as the CFP and CHP units still adopt the control scheme of maintaining minimum output instead of shutting down during the 24h dispatch period [184]. Based on the profiles of energy loads and low-wind power in Fig.…”