Planning and operation scheduling of PV-battery systems: a novel methodology

Rajab Khalilpour, Anthony Vassallo

    Research output: Contribution to journalArticleResearchpeer-review

    95 Citations (Scopus)


    The aim of this paper is to develop a decision support tool for investment decision making, optimal sizing, and operation scheduling of grid-connected PV/battery system with respect to dynamics of periodical weather data, electricity price, PV/battery system cost, PV/battery specifications, desired reliability, and other critical design and operational parameters. We have reviewed the literature on historical development of models for PV-battery systems sizing. A multi-period mixed-integer linear program (MILP) is then introduced with the objective of maximizing the net present value of cash flow (for investment analysis) or the savings in electricity bill (for operation scheduling). For investment decision analysis, the model is capable to identify whether it is economical to invest in PV and/or battery systems, and if positive it can find the best PV and/or battery systems from the mix of available options. Furthermore, the model defines the optimal size of selected PV and/or battery systems. The model will select one or a combination of a few systems if feasible. All these decision variables are identified concurrently with finding the optimal operation schedule of the PV and/or battery systems at each period over the planning horizon. These variables include power flows of grid-to-load, PV-to-load, battery-to-load, battery-to-grid, grid-to-battery, PV-to-battery, PV-to-grid as well as battery state-of charge. This decision support program enables the consumer (ranging from a small house to large-scale industrial plants) to implement the most efficient electricity management strategy while achieving the goal of minimizing the electricity bill.

    Original languageEnglish
    Article number4816
    Pages (from-to)194-208
    Number of pages15
    JournalRenewable and Sustainable Energy Reviews
    Publication statusPublished - Jan 2016


    • Battery
    • Distributed generation
    • Energy storage
    • Planning and scheduling
    • Polygeneration
    • Solar photovoltaic

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