Two-stage optimal scheduling of air conditioning resources with high photovoltaic penetrations

Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; Guangdong Foshan Power Construction Corporation Group Co., Ltd

Demand response from the control of aggregated inverter air conditioners

Inverter air conditioners (ACs) account for a large proportion of air conditioning loads in many countries and, thus, contribute significantly to the peak loads in these areas, especially in summer. On the other hand, as an important category of thermostatically controlled load with thermal energy storage capability, inverter ACs also have the potential to provide considerable flexibility for electric power systems that are faced with increasing challenges posed by high penetration of renewable power generation. This paper focuses on the demand response from the control of the aggregated inverter ACs for load reduction. A virtual energy storage system (VESS) model that encapsulates the room with an inverter AC was established based on the electric model of an inverter AC and the thermodynamic model of a room. Based on the VESS model, a virtual state of charge (VSOC) priority-based load reduction control method with temperature holding and linear recovery strategies was proposed. The VSOC priority based control was designed to decrease the negative impact of load reduction on customers’ thermal comfort from the perspective of the whole AC population. The temperature holding strategy was designed to reduce the electric power of an AC while ensuring that the indoor temperature is always below the allowable limit. The linear recover strategy was proposed to reduce the load rebound after load reduction. Four cases were studied regarding the operation and load reduction of the 100 inverter ACs, and the simulation results verified the models established and the effectiveness and advantages of the proposed load reduction control method

Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the autocorrelation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively