Community Solar

Community solar (CS) involves the use of shared solar photovoltaic and energy storage systems within a local community, a concept that has grown in popularity in recent years. In this project, we aim to facilitate the coordination of decentralized CS investments through dynamic pricing and to improve the performance of CS by implementing land use regulation controls.
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Singapore Neighbourhoods. Image from the presentation at the

Singapore Neighbourhoods. Image from the presentation at the "Urban Solutions and Sustainability congress 2023", Singapore.

Socioeconomic factors in PV Deployment

Corresponding Author:

- Dr Jidong Kang, david-kjd@live.cn

Publications: 
- Kang JD, Fu YM, Mosteiro-Romero, M, and Waibel C (Under review at Energy Economics). “From centralized to decentralized expansion planning of energy community: A stochastic-robust bilevel optimization approach”. Download Manuscript here
- Kang JD, Cai CY, Waibel C, Herthogs P, and Fu YM (In preparation). “Quantifying the impacts of granular land use regulations on distributed solar energy system performance”. 
- Wu ZC, Kang JD, et al. (2024). "A distributionally robust optimization model for building-integrated photovoltaic system expansion planning under demand and irradiance uncertainties". Applied Energy 372(123740). https://doi.org/10.1016/j.apenergy.2024.123740

Presentations:
- Kang JD, Mosteiro-Romero M, Fu Y, Waibel C. "Adoption of PV in Solar Neighbourhoods". Urban Solutions and Sustainability - R&D Congress 2023, Singapore
- Kang JD, Cai C, Waibel C, Fu Y. "Investigation of the Impact of Urban Planning Regulations on Distributed Energy Systems Performance". World Cities Summit 2024 Singapore.
- Kang JD, Cai C, Waibel C, Fu Y. "How Land-Use Regulation Impact Distributed Energy Systems: Integration of Energy System Optimization and Digital Urban Regulatory Models". International Conference on Applied Energy 2024, Niigata City, Japan.


Figure 1. Existing business models for solar PV investment in Singapore.

Figure 1. Existing business models for solar PV investment in Singapore.

Coordinating decentralized CS investment via dynamic pricing

Community solar, an innovative business model for solar energy sharing, has gained widespread attention in recent years. While optimization has been extensively used for optimal capacity expansion planning in energy community, most studies focused on “centralized expansion planning”: a central planner decides on technology selection, sizing, and operation for the distributed energy resources (DERs) in the entire community. In this paper, on the other hand, we propose a stochastic-robust bilevel optimization approach to investigate a decentralized solution that aims to incentivize individual prosumers to invest and manage DERs on their owns but in a coordinated way, and we call it “decentralized expansion planning". We achieve it by modelling dynamic internal energy prices from the member’s individual energy supply and demand, therefore also acting as incentivize signals for private DER investment. Guided by such price signals, capacity investment and operation in the community is coordinated decentralized but towards a social optimum. The decentralized expansion planning problem under stochastic-robust setting is proven to be equivalent to a centralized problem that can be solved efficiently.


We demonstrate the approach to a real-world building cluster in Singapore. Our results show that the proposed decentralized expansion planning model of CS can create positive incentives for all participants when compared to alternative business models (see Fig. 1) for solar investment. However, the incentives allocated to different participants vary significantly due to differences in their photovoltaic (PV) generation potential and energy demand. The modelling results also indicate that the incentives from CS can be significantly improved through integration of advanced energy technologies, e.g. battery storage and district cooling.

Figure 2. Integration of digital urban regulatory and energy system optimization models for land use and energy system planning

Figure 2. Integration of digital urban regulatory and energy system optimization models for land use and energy system planning

Enhancing CS performance via land use regulations controls


Previous studies have established a significant link between urban form and community solar (CS) performance. However, urban development is often constrained by land use regulations (LURs), which govern key factors such as allowable land uses per plot and allowable gross floor area (GFA) per land use. The impact of such plot-level LURs on CS performance has been seldom studied. This study aims to fill this research gap by exploring how the plot-level LURs affect CS performance and how CS performance can be enhanced through LUR controls. To answer the research questions, we develop a methodology (see Fig. 2) that supports urban planners and policymakers to integrate energy performance and tangible plot-level LURs during the urban planning process. By linking an energy and urban form optimization model with a digital urban regulatory model, which provides digitalised urban planning restrictions for cities, we examine the impacts of granular LURs on CS performance at the scale of urban plots.

The results from a case study of a mixed-use district in Singapore indicate that implementing the proposed land use configurations could lead to a 25% reduction in annual energy costs per GFA, an 8% increase in annual energy self-sufficiency, and a 26% decrease in hourly peak load. Further, a sensitive analysis shows that CS performance is particularly sensitive to LUR control in specific land uses, especially those with a high share of residential use zones. The improvements in CS performance are mainly driven by reduced electricity load due to the deployment of energy-efficient buildings, increased solar generation potential from reduced shading, and enhanced demand response potential resulting from greater load diversification in mixed-use districts.

Published 09. October 2024 (Updated 1 month ago) Return POW Module Overview

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