Details

Autor(en) / Beteiligte

• Qin, Chao
• Yan, Qingyou
• He, Gang

Titel

Integrated energy systems planning with electricity, heat and gas using particle swarm optimization

Ist Teil von

• Energy (Oxford), 2019-12, Vol.188, p.116044, Article 116044

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• An integrated energy system combines the power grid, natural gas pipeline, district heating network, and renewable energy generation to enhance the integration of renewable energy and smooth the load demand profile. However, the system faces great uncertainty derived from flexible renewable generation and demand load, etc. This paper brought in the robust optimization theory, considered the wind power integration on the supply side and the load fluctuation on the demand side. It also combined the constraints coming from the power grid, natural gas pipeline and heating network. We constructed a multi-objective robust optimization model for integrated energy system, based on minimizing the fuel cost, the wind power curtailment and the variance of peak-valley electrical load on the end-user side, as the objection functions. To solve the global optimal solution of the model, particle swarm optimization algorithm is utilized because of its fast convergence speed. Tianjin was selected as an example to demonstrate the model. Results indicated that, in the scenario of government promoting electricity substitution, the ratios of energy conversion have been optimized. For instance, in recent years, the shares of outsourced electricity, power to heat, and gas to heat are gradually improved toward the optimization results (31.29%, 16.49%, 13.56%). However, the results also implied that the thermal power generation input-output in thermal power plants (heat to power) should be increased, and the ratio of generation from gas-fired units (gas to power) need to be steadily adjusted. The optimization results provide a good reference for the energy investment strategy. •The multi-objective robust optimization model is established.•The uncertainty analysis of the model is proposed.•New power load considering power to heat load, heat to power load and outsourced electricity is calculated.•Energy conversion ratios simulation analysis is demonstrated.