Sequential Tasks Shifting for Participation in Demand Response Programs

In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a laundry room equipped with three washing machines, two dryers, and one iron. A sensitivity analysis of the power consumption limit is presented. The results show that the proposed methodology is able to accommodate the implemented scenario, respecting user preferences and demand response program, minimizing energy costs. The final electricity price has been calculated for all scenarios to discuss the more effective schedule in each scenario.This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.info:eu-repo/semantics/publishedVersio

Sequential Tasks Shifting for Participation in Demand Response Programs

In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a laundry room equipped with three washing machines, two dryers, and one iron. A sensitivity analysis of the power consumption limit is presented. The results show that the proposed methodology is able to accommodate the implemented scenario, respecting user preferences and demand response program, minimizing energy costs. The final electricity price has been calculated for all scenarios to discuss the more effective schedule in each scenario.This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.info:eu-repo/semantics/publishedVersio

Techno-economic methods for analyzing the energetic and economic effects of solar, storage, and demand response

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, 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.Mechanical Engineerin

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Software-Defined Lighting.

For much of the past century, indoor lighting has been based on incandescent or gas-discharge technology. But, with LED lighting experiencing a 20x/decade increase in flux density, 10x/decade decrease in cost, and linear improvements in luminous efficiency, solid-state lighting is finally cost-competitive with the status quo. As a result, LED lighting is projected to reach over 70% market penetration by 2030. This dissertation claims that solid-state lighting’s real potential has been barely explored, that now is the time to explore it, and that new lighting platforms and applications can drive lighting far beyond its roots as an illumination technology. Scaling laws make solid-state lighting competitive with conventional lighting, but two key features make solid-state lighting an enabler for many new applications: the high switching speeds possible using LEDs and the color palettes realizable with Red-Green-Blue-White (RGBW) multi-chip assemblies. For this dissertation, we have explored the post-illumination potential of LED lighting in applications as diverse as visible light communications, indoor positioning, smart dust time synchronization, and embedded device configuration, with an eventual eye toward supporting all of them using a shared lighting infrastructure under a unified system architecture that provides software-control over lighting. To explore the space of software-defined lighting (SDL), we design a compact, flexible, and networked SDL platform to allow researchers to rapidly test new ideas. Using this platform, we demonstrate the viability of several applications, including multi-luminaire synchronized communication to a photodiode receiver, communication to mobile phone cameras, and indoor positioning using unmodified mobile phones. We show that all these applications and many other potential applications can be simultaneously supported by a single lighting infrastructure under software control.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111482/1/samkuo_1.pd

Values and value in design

Relatively little is known about how concepts of human values and value interact during the construction design process. Whilst researchers of value management have expounded in this context upon the complexity of the design process, problem-solving and sense-making, little is said about the alignment and reconciliation of multiple-stakeholder values and value judgements. An abductive reasoning and a grounded theory approach was adopted that iterated between literature and empirical observation to obtain new insights. The initial phase created a values and value framework and Value in Design (VALiD) approach through seven unstructured interviews, a design workshop, four Schwartz Values Surveys (with 545 participants) and 55 semi-structured interviews. The values and value parts were then separately implemented, developed and validated through action research on five live education capital projects, involving over 250 participants. Subsequently, a middle-range theory of values and value is proposed through theoretical triangulation. This draws on seven related theories to provide greater explanatory pluralism, uncover hidden phenomena and enable convergence. The research findings are significant in focusing soft value management on underlying stakeholder values and subjective value judgements. A more nuanced and intertwined relationship between stakeholder values, attitudes, behaviours and qualities during the design process is offered that promotes compromise and sense-making