Power systems balancing with high penetration renewables: The potential of demand response in Hawaii

doi:10.1016/j.enconman.2013.07.056

Energy Conversion and Management

Volume 76, December 2013, Pages 609-619

https://doi.org/10.1016/j.enconman.2013.07.056 Get rights and content

Highlights

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Demand response for Oahu results in system cost savings.
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Demand response improves thermal power plant operations.
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Increased use of wind generation possible with demand response.
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WILMAR model used to simulate various levels and prices of demand response.

Abstract

The State of Hawaii’s Clean Energy policies call for 40% of the state’s electricity to be supplied by renewable sources by 2030. A recent study focusing on the island of Oahu showed that meeting large amounts of the island’s electricity needs with wind and solar introduced significant operational challenges, especially when renewable generation varies from forecasts. This paper focuses on the potential of demand response in balancing supply and demand on an hourly basis. Using the WILMAR model, various levels and prices of demand response were simulated. Results indicate that demand response has the potential to smooth overall power system operation, with production cost savings arising from both improved thermal power plant operations and increased wind production. Demand response program design and cost structure is then discussed drawing from industry experience in direct load control programs.

Introduction

The State of Hawaii has adopted an aggressive renewable portfolio standard of 40% renewable electrical energy by 2030. Meeting this target may require up to 500 MW of wind power being supplied to the Oahu grid from multiple locations, greatly increasing the supply of intermittent electricity generation. Most system balance studies in Hawaii have focused on expensive spinning reserves of fossil generation or electricity storage technologies to provide electricity when generation from renewable sources varies.

However, demand response is an additional option by which the grid operator may ensure the balance of supply and demand by managing loads within and across hours. Demand response tactics include changing air conditioner set points, turning off lights, and—in the future—managing the charging cycles of electric vehicles. Demand response may represent a lower-cost, nearer-term solution for balancing variable supplies than the current set of energy storage options, and thus enable the State of Hawaii to achieve its goals of reducing fuel imports, reducing emissions, and promoting a greener local economy.

Using the recent Oahu Wind Integration and Transmission Study (OWITS) [19] as a guide, this project uses the Wind Power Integration in Liberalized Electricity MARkets (WILMAR) unit commitment model to analyze the system impacts and potential value of demand response in the context of the Oahu electricity sector in 2030. The study incorporates electricity demand and wind speed data, and thermal generation constraints. Additionally, this study reviews experiences from recent direct load control (DLC) programs, in order to help design future demand response programs such that their potential can be captured, and reliably implemented by local electric companies. DLC programs have traditionally been used for emergency operation purposes, in order to reduce demand quickly due to a supply–demand mismatch on the system. This paper focuses on a less critical application of demand response, “balancing demand response” which provides additional flexible options that allow the grid operators to manage unanticipated changes in the output of renewable generation.

While this study looks primarily at the potential role of demand response in managing a large increase in the supply of wind generation to Oahu, demand response may have broader benefits in balancing electricity supplies and demands when there are additional fluctuations due to other renewables and smart loads including electric vehicle charging.

Section snippets

Overview

In January of 2008, the State of Hawaii and the U.S. Department of Energy signed a Memorandum of Understanding (MOU) which led to the establishment of the Hawaii Clean Energy Initiative (HCEI). The HCEI, a stakeholder led process, set goals for renewable electricity, energy efficiency, transportation, and fuels, with an overall goal of using 70% clean energy by 2030. The renewable electricity goals were codified in the renewable energy portfolio standard, which requires utilities to produce 40%

Wind and demand response scenarios for Oahu in 2030

This study analyzes the operational impacts of demand response. In addition, it study asks two main questions:

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What is the optimal level of demand response for the Oahu grid?
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What is the optimal price to activate the needed demand response resources?

The study answers these questions by performing a stochastic simulation of the Oahu electrical grid with varying quantities of demand response quantity and price. The team simulated twelve full-year demand response scenarios of the Oahu electrical

Demand response results for Oahu in 2030

The metrics for electrical system performance using the WILMAR model were (1) annual system operational costs, (2) reliability, and (3) impact upon participants.

Lessons learned from demand participation programs

Simulation results draw attention to the actual types of customers and loads participating in demand response programs, and the overall design of demand response programs. In order to capture the potential value of demand response highlighted by these simulations, real world demand response programs must be robust, and bankable in the eyes of the grid operator. Otherwise, thermal units are likely to continue to be dispatched as they are today. Historically, demand participation programs have

Summary and discussion

This study used the WILMAR model to simulate the effects of enhanced demand response on a future electrical grid configuration in Hawaii. It compared twelve scenarios representing different participation and pricing of demand response. The results estimated the potential of an expanded EnergyScout program to help the power system accept the output of 500 MW of installed wind capacity in 2030 as proposed in the OWITS [43]. While the motivation for the study was to see how demand response might

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