A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage

Abstract

With the advance of China’s power system reform, combined heat and power (CHP) units can participate in multi-energy market. In order to maximize CHP profit in a multi-energy market, a bidding strategy for deep peak regulation auxiliary service of a CHP based on a two-stage stochastic programming risk-averse model and district heating network (DHN) energy storage was proposed. The quotation set of competitors and load uncertainty was modeled with a Latin hypercube sampling (LHS) method. A dynamic queuing method was used to clear the market for the deep peak regulation auxiliary service to determine the bidding capacities of CHPs in the electricity market and the deep peak regulation auxiliary service market, respectively. Finally, the conditional value-at-risk (CVaR) indicator is used to measure the risk brought by the system uncertainty to the CHP, and the quotation coefficient is determined after considering the expected profit and risk profit comprehensively. The results of the example show that the profits produced by simultaneous participation in both electricity market and the deep peak regulation auxiliary service market are increased by approximately 9.5% compared with the profits produced by only participation in a single market. In addition, the use of DHN energy storage led to a profit increase of approximately 4.6%. As the risk aversion coefficient increases, the expected profit will be further reduced.

Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility

“…They concluded the robust optimal control and the MPC reached about the same performance, but the stochastic OCP could do so with a single evaluation across the whole time horizon. Tian [17] optimised the operation of a CHP that was connected to both the electricity system and a DH system with a two-stage stochastic problem. The goal was to offer ancillary services and participate in the electricity spot market, while the electricity demand is uncertain.…”

“…There is operational heat pump optimisation, in which the interaction with the day-ahead market is optimised, and peak shaving, in which the use of an expensive and/or polluting peak unit is minimised [8][9][10]. In the literature, other applications of network flexibility can be found: CHP optimisation [11][12][13], R 2 ES integration [14][15][16], and providing ancillary services [17].…”

Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method

“…The penalty cost mainly includes the load shedding penalty when the actual wind power output being less than the planned output [15], and the wind curtailment penalty when the wind power being greater than the planned output.…”

Research on Down-Regulation Cost of Flexible Combined Heat Power Plants Participating in Real-Time Deep Down-Regulation Market

“…At the system level, the main concerns are the impact of the decoupling on integration of renewable energy, energy consumption and emissions [3][4][5], national economy [6], and centralized dispatch operations [7] after the decoupling. At the power plant level, the main concerns are the impact of decoupling technology [8], operational flexibility evaluation [9][10][11][12], operational strategies and economics [13][14][15][16][17]. However, in the context of the Northeast China DDR market, there is little literature on the operational strategies of CHP plants after the decoupling.…”

“…However, in the context of the Northeast China DDR market, there is little literature on the operational strategies of CHP plants after the decoupling. Only one of them [16] studies the bidding strategy of plants with extraction condensing units and HA through predicting the behavior of competitors, and obtains very useful conclusions. However, they do not consider multitypes of units and multiple decoupling methods.…”