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Communication Dans Un Congrès Année : 2020

Impact of the Economic Environment Modelling for the Optimal Design of a Multi-Energy Microgrid

Résumé

The paper discusses the impact of the problem formulation for the optimal design of a multi-energy microgrid. The study shows that the energy rates affect the output results much more than any other parameter such as the cost of equipment or the assets efficiencies (ratio 1 to 100). The main focus of the paper is then the representation of the economic environment for such a planning problems. Five different modeling approaches are investigated with both deterministic and stochastic methods (scenario-based formulation), as well as constant or increasing rates along the planning horizon. The obtained results show great impacts on the installed capacities (variations from simple to triple for the gas engine rated power), while the objective function (i.e. system cost of ownership) displays small variations less than 5 % with the different sets of hypothesis. The papers then concludes on a recommendation to present the results in terms of optimal areas instead of a single global optimum for such problems. I. NOMENCLATURE Operating Variables : P t ge , M t ge gas engine power/fuel at time t (kW , kg/h) P pv solar generation at time t (kW) P t bat+ , P t bat− battery charge/discharge at time t (kW) P t gd grid power at time t (kW)-generator conv. P gdP grid peak power along the emulated period (kW) P t ch chiller plant electrical load at time t (kW) P t abch absorption chiller electrical load at time t (kW) Q t ch chiller plant cooling power at time t (RT) Q t abch absorption chiller cooling power at time t (RT) Q t ts+ , Q t ts− charging/discharging power of ts at time t (RT) SOC t bat , SOC t ts battery/th. storage state of charge at time t (%) Sizing Variables: P geR , P pvR gas engine/solar gen. rated powers (kW, kWp) E batR , E tsR battery/th. storage capacities (kWh-RTh) Q tsR thermal storage rated power (RT) Parameters: P t l , Q t l electrical/thermal load at time t (kW, RT) P t pvN normalized solar generation at time t (-) π t e ,π p ,π t e elect./peak/gas prices ($/kWh, $/kW, $/kg) α ge , β ge coefficients for gas engine operating cost η bat , η ts battery and thermal storage efficiencies (-) SOC bat , SOC bat ̅̅̅̅̅̅̅̅̅ battery min/max state of charge (%) SOC ts , SOC ts ̅̅̅̅̅̅̅ min/max state of charge of battery ts (%) SOC 0 bat , SOC 0 ts battery and thermal storage initial SOC (%) COP ch chiller plant coefficient of performance (-) COP abch absorption chiller coefficient of performance (-) β pwh abch absorption chiller power/heat ratio (RT/kW) β pcr abch absorption chiller cooling ratio (kW/RT) II. INTRODUCTION Multi energy systems have long been proved to enhance the integration of renewable energy sources by providing efficiency and flexibility with the interaction of different energy vectors [1]. The optimal design of so-called "multi-energy microgrids" has been extensively addressed in the literature. Such problems consist in finding the best capacities of the assets (solar generator, co-generation plant, electrical/thermal storages, etc) with a tradeoff between the capital expenditures and the operating cost estimated along the system lifetime [2]. Such decision processes are subject to a wide range of uncertainties that has to be taken into consideration in the design phase-uncertainties regarding the load level, the renewable generation, the energy prices, the investment costs as well as the models accuracy [3]. Considering those uncertainties may lead to prohibitive computational times is usually tackled with different approaches mixed with simplified linear models of the systems. Typical methods rely on robust optimization with the definition of best/worst case scenarios that represent the volatility of uncertain parameters [4]. Other methods introduce chance constraints while setting a tolerance on the probability to meet specific operating conditions (typically power balance) [5]. Finally, scenario-based stochastic optimization is the most widely used approach. It define sets of potential scenario (with uniform or normal distribution to represent the uncertainties). The objective function is then formulated as the sum of the optimal results for each scenario weighted by the corresponding probability of occurrence [6] [7]. It is important to note that the majority of those studies consider the uncertainties for the load profiles and renewable-based generation. This paper investigates the relevance of the uncertainties consideration on a generic benchmark and with
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hal-03016150 , version 1 (20-11-2020)

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Remy Rigo-Mariani, Sean Ooi Chea Wae, Stefano Mazzoni. Impact of the Economic Environment Modelling for the Optimal Design of a Multi-Energy Microgrid. IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society, Oct 2020, Singapore, Singapore. pp.1837-1842, ⟨10.1109/IECON43393.2020.9254730⟩. ⟨hal-03016150⟩
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