Utility System Capacity And Customer Demand Value Of Photovoltaic

Author : Arkasama Bandyopadhyay
Publisher :
ISBN 13 :
Total Pages : 360 pages
Book Rating : 4.:/5 (125 download)

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Download or read book Techno-economic Methods for Analyzing the Energetic and Economic Effects of Solar, Storage, and Demand Response written by Arkasama Bandyopadhyay and published by . This book was released on 2020 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Growing population, changing climate, urbanization, and rising economic activities have led to an overall increase in electricity demand. Maintaining the balance between supply and this increasing demand often necessitates the usage of old, inefficient, and environmentally-polluting generators as well as the construction of expensive generation, transmission, and distribution infrastructure. Demand response initiatives (e.g. time-varying electricity prices) and distributed energy resources (DERs), like solar photovoltaic panels and onsite energy storage systems, can help offset a portion of this demand while simultaneously reducing harmful emissions. DERs additionally provide a variety of value streams including peak load reduction, energy arbitrage, real time price dispatch, demand charge reduction, congestion management, voltage support, etc. The impact of price-based demand response and DERs at the electricity distribution level is assessed in this dissertation through the following three studies: (1) quantifying the reduction in 4 coincident peak (4CP) loads and Transmission Cost of Service (TCOS) obligations of electric utilities using local distributed solar and storage, (2) evaluating the peak load reduction/shift potential of time-varying electricity pricing in the residential sector, and (3) investigating the combined energetic and economic potential of DERs and time-varying electricity pricing in the residential sector. When the Electric Reliability Council of Texas (ERCOT) peaks for a single 15-minute interval during each summer month between June and September, the loads of individual Distribution Service Providers (DSPs) in the same time interval are recorded. The averages of these DSP loads, defined as 4CP loads [1], are used to calculate TCOS obligations that each DSP must pay Transmission Service Providers (TSPs) in the next calendar year as compensation for using their transmission infrastructure. First, a generalized tool is built to forecast the change of 4CP loads and corresponding TCOS obligations for electric utilities within ERCOT based on varying amounts of solar and storage capacity. The tool is illustrated by using empirical electricity demand data from the municipally-owned utility in Austin, TX (Austin Energy) and solar generation data from the PVWatts calculator developed by the National Renewable Energy Laboratory. TCOS obligations can be on the order of tens of millions of dollars. Results indicate that solar and storage capacity can substantially lower these payments. For example, a 20 MW increase in local solar capacity in 2018 would reduce Austin Energy’s payment by an estimated $180,000 for each subsequent year. By using the novel approach of incorporating coincident peak demand charge reductions at the distribution level, the economic value of local generation and storage is highlighted. Next, a convex optimization model is developed to analyze the potential for time-varying electricity rate structures to reduce and/or shift peak demand in the residential sector. In this model, a household with four major appliances minimizes electricity costs, with marginally increasing penalties for deviating from temperature set-points or operating appliances at inconvenient times. The four specific appliances included are: heating, ventilation and air-conditioning (HVAC) systems, electric water heaters (EWHs), electric vehicles (EVs), and pool pumps (PPs). The study incorporates a one-parameter thermal model of the home and the electric water heater, so that the penalties can apply to the room and water temperatures rather than the total appliance loads. Analysis is performed on a community of 100 single-family detached homes in Austin, TX. These homes each host a combination of the four end-use devices while some also have onsite solar panels. Results show that dynamic pricing effectively shifts the residential peak away from the time of overall peak load across the electricity system, but can have the adverse impact of making the residential peak higher. The energy consumption does not differ significantly across the different rate structures. Thus, it can be inferred that the time-varying rates encourage customers to concentrate their electricity demand within low-price hours to the extent possible without incurring significant inconvenience. By incorporating the novel approach of including monetary value of customer behavior in price-based demand response models, this study builds a tool to realistically quantify peak load reduction and shifts in the residential sector. Finally, the convex optimization model is extended to consider larger sets of distributed technologies that might be deployed in homes and investigate how different combinations of these technologies affect peak grid load, energy consumption from the grid, and emissions in the residential sector under time-varying pricing structures. In the model, households with varied amalgamations of distributed energy technologies minimize electricity costs, amortized capital, and operational costs over a year, with marginally increasing penalties for deviating from room temperature set-points. The four technologies considered are: solar photovoltaic (PV) panels, lithium-ion batteries, ice cold thermal energy storage (CTES), and smart thermostats. Results show that from an economic perspective, it is optimal for residential customers to install solar panels under tiered rates, time-of-use rates, and critical peak prices while it is cheapest to own a combination of solar panels and smart thermostats when real-time prices and demand charges are in effect. The capital and installation costs of both storage systems are still too high to make them economically profitable investments for typical residential customers. Additionally, solar panels are the main instruments to reduce energy purchased from the grid and carbon dioxide emissions under all pricing schemes. Adding smart thermostats can reduce these metrics to a greater extent by making the home energy-efficient. Further, while the energetic effect of the two storage systems can be favorable or detrimental depending upon the load profile of the particular household and the pricing structure, lithium-ion batteries are the main instruments to avoid high demand charges by spreading the demand in the home (and power bought from the grid) evenly to the extent possible without incurring significant customer discomfort. Thus, this study recommends that residential customers invest in solar panels and smart thermostats to minimize overall annual expenditure and make their homes environmentally efficient. Further, as an effective peak load control mechanism, electric utilities should offer significant rebates to encourage residential customer investment in storage systems in addition to subjecting them to demand charges. Electricity generation from intermittent renewable energy sources has grown rapidly worldwide. DER installation levels continue to rise with the decline in capital costs of energy storage systems and local renewable generation assets, the growth of supportive government policies, and rising concerns about climate change among the masses. Additionally, electric utilities are increasingly employing demand response initiatives to curtail and/or shift peak demand. As a whole, the body of work developed in this dissertation can be used by electric utilities to make optimal decisions about dynamic rate design and policies for increased DER adoption. It can also be used by residential electricity customers to maneuver their own energy consumption patterns and assess the economic viability of investing in DERs