Optimization of Operation Strategies for a Combined Cooling, Heating and Power System based on Adiabatic Compressed Air Energy Storage

The fluctuations of renewable energy and various energy demands are crucial issues for the optimal design and operation of combined cooling,heating and power(CCHP)system.In this paper,a novel CCHP system is simulated with advanced adiabatic compressed air energy storage(AA-CAES)technology as a join to connect with wind energy generation and an internal-combustion engine(ICE).The capital cost of utilities,energy cost,environmental protection cost and primary energy savings ratio(P E S R)are used as system performance indicators.To fulfill the cooling,heating and power requirements of a district and consider the thermal-electric coupling of ICE and AA-CAES in CCHP system,three operation strategies are established to schedule the dispatch of AA-CAES and ICE:ICE priority operation strategy,CAES priority operation strategy and simultaneous operation strategy.Each strategy leads the operation load of AA-CAES or ICE to improve the energy supply efficiency of the system.Moreover,to minimize comprehensive costs and maximize the P E S R,a novel optimization algorithm based on intelligent updating multi-objective differential evolution(MODE)is proposed to solve the optimization model.Considering the multi-interface characteristic and active management ability of the ICE and AA-CAES,the economic benefits and energy efficiency of the three operation strategies are compared by the simulation with the same system configuration.On a typical summer day,the simultaneous strategy is the best solution as the total cost is 3643 USD and the P E S R is 66.1%,while on a typical winter day,the ICE priority strategy is the best solution as the total cost is 4529 USD and the P E S R is 64.4%.The proposed methodology provides the CCHP based AA-CAES system with a better optimized operation.

Risk Constrained Self-scheduling of AA-CAES Facilities in Electricity and Heat Markets:A Distributionally Robust Optimization Approach

Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage technology in a low-carbon society.An appropriate self-scheduling model can guarantee AA-CAES’s profit and attract investments.However,very few studies refer to the cogeneration ability of AA-CAES,which enables the possibility to trade in the electricity and heat markets at the same time.In this paper,we propose a multimarket self-scheduling model to make full use of heat produced in compressors.The volatile market price is modeled by a set of inexact distributions based on historical data through-divergence.Then,the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory,and equivalently reformulated as a mixed-integer linear program(MILP).The numerical simulation results validate the proposed method and demonstrate that participating in multienergy markets increases overall profits.The impact of uncertainty parameters is also discussed in the sensibility analysis.