Saturation-phase prediction of building-integrated photovoltaics by using agent-based simulations

Abstract

Saturation phase of building-integrated photovoltaics (PV) is described, for which multi-agent simulations coupled with electric power flow analysis are carried out. The detailed power flow analysis determines the probability of reverse current occurrence due to the additional PV installation in a 6.75 kV-class power network consisting of 2500 grid-connected individual customers. The agent-based simulation including the customer agent and government agent describes the timedependent behavior of PV saturation-phase in an artificial society as determined through the power flow analysis. Growth in share of PV is assisted by a battery installation as a reservoir for the reverse power current. On the other hand, the excessive promotion for PV installation by the government results in the increase in the cost of reverse current care. I. INTRODUCTIONIntegration of renewable energy sources in power networks is an emerging subject of electrical engineering as related to dispersed generation technology and environmental engineering. To design a survivable society in 21st century, technology and human nature have to be in harmony. As an instrumentality of energy sciences, a working project of "complexity-based emergent approach to survivable energy¢environment design (cSEED)" has been in progress [1] [6]. Figure 1 shows a conceptual illustration of a virtual society consists of renewable energy sources and an electrical power network. Solar energy, wind energy, and biomass energy are integrated in a power network with power plant, transmission facility, and energy storage system, so that they can interact with human society (customer).The focus of this work is to predict the saturation-phase of building-integrated photovoltaics (PV) in an artificial virtual society by using multi-agent simulations coupled with electric power flow analysis. Complex system is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment [7], [8]. The multi-agent system is one of the computational models for simulating the actions and interactions of autonomous individuals with a view to assessing their effects on the system as a whole [9], [10], [11]. The present agent-based models includes Two kinds of agent, that is, customer agents and a government agent. Ability of decision-making and learning rules for customer agents.

A Multi-Agent Social Gamification Model to Guide Sustainable Urban Photovoltaic Panels Installation Policies

“…Murakami described saturation phase of building-integrated PVs and coupled multi-agent simulations with electric power flow analysis. The presented agent-based model included two kinds of agents-customer and government agents [17]. Massey et al described an online map-based gaming platform, which simulates spatial scenarios as MAS (multi-agent system) using human participants as the decision agents [18].…”