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Co-Optimization of Generation Expansion Planning and Electric Vehicles Flexibility

handle: 10044/1/34207
The envisaged de-carbonization of power systems poses unprecedented challenges enhancing the potential of flexible demand. However, the incorporation of the latter in system planning has yet to be comprehensively investigated. This paper proposes a novel planning model that allows co-optimizing the investment and operating costs of conventional generation assets and demand flexibility, in the form of smart-charging/discharging electric vehicles (EV). The model includes a detailed representation of EV operational constraints along with the generation technical characteristics, and accounts for the costs required to enable demand flexibility. Computational tractability is achieved through clustering generation units and EV, which allows massively reducing the number of decision variables and constraints, and avoiding non-linearities. Case studies in the context of the U.K. demonstrate the economic value of EV flexibility in reducing peak demand levels and absorbing wind generation variability, and the dependence of this value on the required enabling cost and users’ traveling patterns.
- Imperial College London United Kingdom
Technology, Science & Technology, mixed-integer programming, 0906 Electrical And Electronic Engineering, 0915 Interdisciplinary Engineering, MARKETS, CAPACITY, Engineering, DEMAND RESPONSE, Demand flexibility, SYSTEMS, Electrical & Electronic, vehicle-to-grid, unit commitment, generation expansion planning, electric vehicles
Technology, Science & Technology, mixed-integer programming, 0906 Electrical And Electronic Engineering, 0915 Interdisciplinary Engineering, MARKETS, CAPACITY, Engineering, DEMAND RESPONSE, Demand flexibility, SYSTEMS, Electrical & Electronic, vehicle-to-grid, unit commitment, generation expansion planning, electric vehicles
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).50 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% visibility views 6 download downloads 107 - 6views107downloads
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