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The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the performance of the rooftop photovoltaic (PV) system, taking into account its shading impact on the rooftop surface and the eventual cooling load of the building. This paper tests three approaches of the rooftop PV system: zero tilt angle flat PV configuration; PV configuration with a monthly adjusted tilt angle; and PV configuration with a dual-axis sun tracking system. Each of the PV configurations is optimized to do the following: minimize the self-shading among the adjacent arrays; maximize the rooftop surface shading to curtail the cooling load; maximize the net energy yield; and minimize the net levelized cost of energy (LCOE) of the PV system. The existing building model is developed in SketchUp Pro. The model is simulated in an EnergyPlus environment to calculate the building's cooling energy consumption with different shading scenarios in various PV configurations. Various rooftop PV configurations are designed and simulated in a System Advisor Model (SAM) to analyze the effect of self-shading of the adjacent PV arrays on the PV performance. The optimal distance between the arrays (for maximum net energy yield and minimum net LCOE) is found to be 1.5 m. The net LCOE of the optimal scenario is 5.247 ¢/kW h and 4.112 ¢/kW h for monthly tilt and dual-axis tracking arrangements, respectively. The economic surplus of the optimized system is 0.422 ¢/kW h and 0.258 ¢/kW h for the monthly tilt and dual-axis tracking arrangements, respectively, as compared to the ground-mounted system. [ABSTRACT FROM AUTHOR]

Due to the higher energy efficiency as well as the higher flexibility and reliability of energy supply, multi-carrier energy systems (MCESs) have attracted extensive attention. However, the power imbalance caused by uncertainty, the complicated couplings among multiple carriers, and the communication obstacles among multiple MCESs bring challenges to the synergistic scheduling of multiple MCESs. To this end, this paper studies the synergistic scheduling of multi-MCES coupled with electricity, natural gas, heating, and cooling carriers, considering integrated demand response (IDR) program and load volatility. First, an IDR program with electricity, heating, and cooling loads is proposed, where the consumers can change their energy consumption patterns in response to price signals to reduce operating costs. Second, to smooth the electricity load curve and avoid load fluctuations, load volatility is taken into account when implementing the IDR program. In addition, the non-convex constraints of natural gas network are tackled via second-order cone relaxation, and the scheduling of multi-MCES is reformulated as mixed integer second-order cone programming (MISOCP). Finally, the synergistic scheduling model of multi-MCES considering IDR program and load volatility is solved by consensus-based alternating direction method of multipliers with limited boundary information exchange with neighboring MCESs. Numerical results illustrate the effectiveness of the proposed distributed IDR program in a multi-MCES. [ABSTRACT FROM AUTHOR]