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Regional integrated energy system (RIES) can realize multi-energy conversion and complementation so as to improve energy efficiency, which also brings more security risks. The regional integrated energy system security region (RIESSR) is a security analysis method to describe the safe area for the operating points of RIES based on the N-1 guideline. As a controllable device, energy storage (ES) which is installed in the energy hub (EH) plays an important role in improving system security. Therefore, this paper establishes the model of practical security region of RIES integrating energy storage, and studies the impact of ES on total supply capability (TSC) and practical security boundary. Finally, a specific case is set to simulate and verify the model. By comparing the scenario with ES and the scenario without ES, it can be seen that the solution result of TSC increases and the security region extends across quadrant when the RIES is integrated with ES system. The capacity and location of the ES also impact on TSC and RIESSR.

With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately. [ABSTRACT FROM AUTHOR]

With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price. [ABSTRACT FROM AUTHOR]