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Screening Curve Method for Economic Analysis of Household Solar Energy Self-Consumption

Hikaru Hoshino, Yosuke Irie, Eiko Furutani

TL;DR

The paper addresses the economic analysis of household PV self-consumption with battery storage by extending the Screening Curve Method (SCM) to accommodate PV intermittency through a time-varying load-slice framework. It develops an analytical approach to compute screening curves for grid-only, PV-only, and PV-with-battery options, and derives PV and battery sizes from slice-level costs, complemented by a verification against LP solutions. The authors demonstrate that the SCM can reproduce LP results with high accuracy in typical scenarios and with far superior computational speed, enabling rapid sensitivity analyses and policy-oriented insights. The method offers intuitive cost curves that elucidate drivers of optimal capacities and supports quick exploration of incentive policies, while noting limitations such as handling discrete capacities and discharging dynamics. Overall, the SCM extension provides a practical, scalable tool for researchers and regulators to evaluate and compare PV self-consumption schemes across diverse data sets and parameter settings.

Abstract

The profitability of solar energy self-consumption in households, the so-called photovoltaic (PV) self-consumption, is expected to boost the deployment of PV and battery storage systems. This paper develops a novel method for economic analysis of PV self-consumption using battery storage based on an extension of the Screening Curve Method (SCM). The SCM enables quick and intuitive estimation of the least-cost generation mix for a target load curve and has been used for generation planning for bulk power systems. In this paper, we generalize the framework of existing SCM to take into account the intermittent nature of renewable energy sources and apply it to the problem of optimal sizing of PV and battery storage systems for a household. Numerical studies are provided to verify the estimation accuracy of the proposed SCM and to illustrate its effectiveness in a sensitivity analysis, owing to its ability to show intuitive plots of cost curves for researchers or policy-makers to understand the reasons behind the optimization results.

Screening Curve Method for Economic Analysis of Household Solar Energy Self-Consumption

TL;DR

The paper addresses the economic analysis of household PV self-consumption with battery storage by extending the Screening Curve Method (SCM) to accommodate PV intermittency through a time-varying load-slice framework. It develops an analytical approach to compute screening curves for grid-only, PV-only, and PV-with-battery options, and derives PV and battery sizes from slice-level costs, complemented by a verification against LP solutions. The authors demonstrate that the SCM can reproduce LP results with high accuracy in typical scenarios and with far superior computational speed, enabling rapid sensitivity analyses and policy-oriented insights. The method offers intuitive cost curves that elucidate drivers of optimal capacities and supports quick exploration of incentive policies, while noting limitations such as handling discrete capacities and discharging dynamics. Overall, the SCM extension provides a practical, scalable tool for researchers and regulators to evaluate and compare PV self-consumption schemes across diverse data sets and parameter settings.

Abstract

The profitability of solar energy self-consumption in households, the so-called photovoltaic (PV) self-consumption, is expected to boost the deployment of PV and battery storage systems. This paper develops a novel method for economic analysis of PV self-consumption using battery storage based on an extension of the Screening Curve Method (SCM). The SCM enables quick and intuitive estimation of the least-cost generation mix for a target load curve and has been used for generation planning for bulk power systems. In this paper, we generalize the framework of existing SCM to take into account the intermittent nature of renewable energy sources and apply it to the problem of optimal sizing of PV and battery storage systems for a household. Numerical studies are provided to verify the estimation accuracy of the proposed SCM and to illustrate its effectiveness in a sensitivity analysis, owing to its ability to show intuitive plots of cost curves for researchers or policy-makers to understand the reasons behind the optimization results.
Paper Structure (16 sections, 19 equations, 14 figures, 3 tables)

This paper contains 16 sections, 19 equations, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Optimal Sizing Problem
  3. Screening Curve Method
  4. Review of Existing SCMs
  5. Proposed SCM for PV and battery
  6. Overview
  7. Calculation of Screening Curves
  8. Estimation of Battery Capacity and Charging Profile
  9. Verification Study
  10. Base-Case Analysis
  11. Estimation Accuracy under Various Settings
  12. Discussion
  13. Economic Analysis Using Screening Curves
  14. Computational Time
  15. Advantages and Disadvantages
  16. ...and 1 more sections

Figures (14)

  • Figure 1: Optimal PV and battery sizing problem
  • Figure 2: The classical SCM to determine the optimal thermal generation mix when no technical constraints are considered.
  • Figure 3: Definition of load slice
  • Figure 4: Example of screening curves (cost curves)
  • Figure 6: Time-varying scheme of load slicing in the proposed SCM
  • ...and 9 more figures