Environmental and economic optima of solar home systems design: A combined LCA and LCC approach

Abstract

This paper compares the economic and environmental optimal design of Solar Home Systems (SHSs) and explores the role of economic incentives (such as tariffs and technology costs) in approximating the two optima. To achieve that, we present a methodology for the environmental and economic evaluation of grid-connected SHSs: user-scale electric systems involving a photovoltaic (PV) power system and a battery energy storage system. The proposed methodology is based on a mixed integer linear programming (MILP) optimization, life cycle assessment and life cycle costing. This methodological framework is applied to a case study involving a typical SHS installation in Italy. The results of the environmental optimal design brought to the evaluation of a 3.25 kW PV assisted by 8.66 kWh of nickel cobalt manganese batteries, whereas the costs of the SHS are minimized by a small PV system (less than 1 kW). Results underline that the environmental optimal configurations rely on battery technologies, which entails a significant cost compared to the grid connection. In contrast, the economic optimal design solutions is less impactful than the grid mix both from an environmental and economic points of view. Thanks to a reduction of batteries and PV costs, the environmental impact of the economic optimal design is expected to decrease in the future.

Environmental Impact of Solar Home Systems in Sub-Saharan Africa

“…LABs were found to be the weakest part of the SHS and with the highest impact on the LCA, which was also found in previous studies [23][24][25]. While most of the batteries deployed in SHS in SSA rely on the lead-acid technology, Li-ion batteries for SHS are increasing the market share in other regions, such as Europe and North America [58]. Market trend in SSA and the higher capital cost per new battery are factors that explain the relatively slow deployment of Li-ion batteries for SHS in this region.…”

Optimal sizing of Solar Home Systems: Charge controller technology and its influence on system design

“…with the timestep n to N and the polynomial function defined in equation (17). The relative and absolute capacity degradation of each micro-cycle are then calculated with the following equations:…”

“…The tool further uses static component efficiency values and neglects battery degradation [16]. Rossi et al compare the economic and environmental optimal design of grid-connected Solar Home Systems using a mixed-integer linear programming approach with an innovative cross-evaluation analysis [17]. The case-study covers an application in Italy with higher SHS capacities as introduced by [5].…”

Techno-Economic Optimization Analysis of an Autonomous Photovoltaic Power System for a Shoreline Electrode Station of HVDC Link: Case Study of an Electrode Station on the Small Island of Stachtoroi for the Attica–Crete Interconnection

“…The optimization methods for the sizing of PVs and energy storage items, which can be: Simplified for preliminary studies based on a typical daily electric load demand and meteorological data (solar irradiation and ambient temperature) [9][10][11], Numerical iterative, which simulate several combinations of stand-alone photovoltaic (SAPV) components size based on the electric load demand and meteorological data of a typical year in the hourly or daily time step, with one or more objective functions and conditions, as technical (i.e., loss of load probability, loss of energy expected, and PV panel tilt angle), economic (i.e., annualized life cost, net present value, levelized cost of energy, life cycle cost, and life cost unit cost), environmental (i.e., life cycle assessment, embodied energy, and CO 2 emissions), and social (i.e., job creation) [2,[12][13][14], Analytical optimization methods with one or more objective functions and conditions that represent the components of technical and economic specifications with mathematical equations having the additional difficulty of coefficients determination [1,3,8], Non-conventional including a stochastic [15], artificial neural network [16], hybrid with the Pareto method and hierarchy process [17].…”

“…Even if this numerical method looks simple, it should overcome a large number of problems, i.e., the calculation of the capital cost for different PV configurations, which can include various converters, roof expanding, etc. In other relative research works [3,4,[8][9][10][11][12][13][14][15][16][17] the discrete cost of the converters, roof expanding, etc., are ignored using typical capital cost functions for PVs panels, batteries, etc.…”