Date of Award

14-5-2024

Document Type

Thesis

School

School of Electrical & Electroncis Engineering

Programme

Ph.D.-Doctoral of Philosophy

First Advisor

Dr.N.Prabaharan

Keywords

Peer-To-Peer Trading, Distributed Generation, Electric Vehicle, Renewable Energy, Double Auction Mechanism

Abstract

Demand Side Management (DSM) is vital in handling uncertain renewable power generation and demand in a deregulated power system. The flat load profile can be obtained using the Demand Response (DR) techniques with the storage elements and proper switching. The increasing penetration of renewable-based distributed generation (DGs), batteries, and electric vehicles (EVs) supports the DR measures that facilitate utility and consumer.

This research aims to minimize the peak demand, electricity cost, and emission rate by effectively utilizing the storage with Renewable Energy Sources (RES). The first specific aim of the research objective focuses on the impact of DR implementation, DGs such as solar photovoltaic (PV) and wind, battery, and EV in reducing the electricity cost of the consumer. A Smart Home Energy Management System (SHEMS) with Real Time Pricing (RTP)-based Demand Response (DR) is proposed for appliance scheduling in residential consumers using the Binary Particle Swarm Optimization (BPSO) algorithm.

In the proposed SHEMS, 18 different smart appliances, DGs, a battery, and an EV are considered. The smart appliances used in this work are categorized into two types: thermostatically and electrically controllable loads. The smart appliances in the SHEMS are optimally scheduled using the BPSO algorithm based on the RTP, availability of DGs, battery, EV, and consumer preference on appliance operation time window and duration to minimize the electricity cost of the smart home.

Five different cases are examined based on the availability of DGs and battery to show the effectiveness of the proposed SHEMS. An EV has been utilized to enable vehicle-to-grid and vehicle-to-home power transfer, further reducing the smart home’s overall electricity cost. The second specific aim of the research objective focuses on energy trading in the SHEMS.

In recent years, smart consumers, along with DGs, are considered prosumers. The prosumers trade the available excess power to the consumers to minimize their electricity costs. The implementation of peer-to-peer (P2P) energy trading in the smart home further minimizes the electricity cost of the consumer due to the energy trading from prosumers instead of the grid. Also, the burden on the utility during the peak hour is reduced by implementing DR-based P2P energy trading.

The third specific aim of the research objective proposes a smart bidding strategy to further minimize the electricity costs of consumers and increase their profit of prosumers. The smart bidding strategy implements the double auction mechanism by determining the bid, quote, and trading tariff based on the Supply-Demand Ratio (SDR) and grid tariff. Hence, it is beneficial for both prosumers and consumers.

The effectiveness of the proposed smart bidding strategy is proved by including the uncertain EV operation in three cases such as EV uncertainty in first consumer, first prosumer and in both first consumer and prosumer. The simulation results proved that the smart bidding strategy reduced the electricity cost and grid dependency of the smart homes at both normal and EV uncertainty conditions.

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