Investigation of Demand Response Potential in Smart Grids

Najafi, Foad
Fripp, Matthias
Electrical Engineering
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21st-century power systems are being exposed to many new challenges and opportunities at the same time. Climate change and the increasing cost of fossil fuels force us to replace our primary source of energy with renewables. At the same time, new communication technologies such as 5G paves the way for two-way communication between the generation side and the demand side. The two-way communication between two sides provides numerous advantages for the modern power systems to tackle the environmental and economic challenges with new and creative innovations.One of the new ideas that are conceptualized after the emergence of two-way communication between generation and consumption is the demand response (DR). The DR can help us to overcome these challenges in parallel with other technologies such as energy storage systems. The DR program can help improve any objective that we pursue in the power systems. They can help to reduce the operation cost of power systems by redistributing their consumption plans to the times that less energy consumption is expected. They can help to incorporate more renewable energy sources into power systems. Since renewables are not dispatchable, the current power systems structure has difficulty incorporating a larger share of renewables into power systems due to their unpredictable nature. However, demand-responsive loads can alleviate this problem by adapting their consumption plans based on the generation side with a large share of renewables. The DR program can help reduce the capital cost of power system expansion. Each year, governments are forced to build new infrastructure so only to accommodate the annual peak increase that just happens for a couple of days each year. However, with the adaptive demand that could be used to curb the peak load, millions could be saved by reducing the need to build new infrastructures that are built to meet the peak demand. The DR program can help to improve power quality and operational costs. The unit commitment problems are armed with demand-responsive loads, much lower operational cost, and better power quality at the same time. While there are various types of demand control, such as direct load control (DLC), the focus of this research is on price responsive demand (PRD). The PRDs are more advantageous to DLC for various reasons. One reason is the possible discomfort that DLC approaches could bring. I.e., the service could cut off for the user when the user needs energy. However, with the PRD, the user simply pays the higher cost for using the service. Another main advantage that PRD control has over the DLC is the possibility of creating a market. With the two-way communication that is available, the loads can participate in the markets. E.g., currently, there is a surplus of solar production at midday. However, the PRDs could be used to increase their consumption in the middle of the day by responding to the low cost of electricity in those times. They can participate in ancillary services such as frequency and voltage provisioning based on the needs of the market (operator here) and reduce the cost for the user. Given these opportunities the PRDs have, this thesis is focused on exploring the new opportunities that this program can bring to reality. In the literature, appliances are categorized roughly into three main categories: non-shiftable such as TVs, shiftable such EVs, and thermostatically controller appliances TCAs) such as Electric Water Heaters (EWH). There have been many studies to evaluate the potential of shiftable appliances such as EVs. However, due to temperature drop of TCAs, and the relationship between working temperature and the ambient temperatures, the potentials of TCAs as a reliable PRD is not investigated. Among the TCAs, the EWH have the most potentials to be successfully used in DR programming. They have relatively good insulation and can keep the energy for hours. In contrast, HVAC appliances (heating, ventilation, and air conditioning) cannot maintain their temperature for a long time and need to be fed by energy in short periods of time. At the same time, EWH are a large consumer of energy (some sources report up to 40 percent of daily energy usage). These characteristics makes EWHs a good choice for DR programming. The first two section of this thesis is focused on the potential of EWH as a PRD. First capture investigates the effect unpredictability of energy usage for scheduling the EWHs. alongside that, a new approach to model the concept of comfort is introduced. Accurate Scheduling of appliances needs accurate estimation of environmental inputs. However, a 100 accurate prediction is not feasible. To tackle the uncertainty in prediction of variables, researchers proposed to main approaches to deal with uncertainly. One of them is robust optimization and the other stochastic optimization. In the first chapter of the thesis, a stochastic optimization model is proposed to deal with the randomness of user hot water withdrawal. Alongside that, a dual objective model where one of the objectives is the newly conceptualized discomfort cost. Together, the dual objective comfort-based approach combined with stochastic approach, improves the decision-making procedure by a large margin. The propose model proves that is more accurate at prediction user consumption and at the same time, it incurs less discomfort to the user. Chapter two investigates the potential of EWH as solar energy self-absorbing unit. As mentioned before, solar energy production is concentrated mostly on the middle of the day and no production on other times. However, until now, no effective mechanism is used to absorb this excess energy to the grid. An alternative method is the use of batteries as a storage. However, batteries are expensive as of today. An alternative method is increasing solar energy absorption by inducing demand in appliances. One of the best appliances for this matter is the EWHs. due to their large working temperature range, they can behave as a virtual energy storage unit. I.e., they can absorb energy when solar energy production is high and then gives back this energy to the user when they needed it. In chapter two, a new price-responsive EWH model with solar-absorption capacity is introduced. This model which is based on a modified version of the model that has been proposed in chapter one, can optimally schedule EWH. it can take advantage of using free solar energy and increase the PV self-absorption capacity. This price-responsive approach reduces the operational cost by reducing the dependency on the energy from the grid. At the same time, since it can absorb a large chunk for solar energy, it can reduce the need for a large capacity energy storage unit. Thirst chapter investigates the possibility of market bidding mechanism with the presence of PRDs. a market for energy where the energy generators and users bid for the amount of energy that are willing to buy for a given price would be an ideal mechanism than can bring the energy section close to a free market where ha numerous advantages. However, this ideal model did not happen due to numerous reason such as lack of communication infrastructure, inelastic loads and many other reasons. Aside from these physical obstacles, the lack of an effective method to coordinate the PRD is another challenge. One of the main challenges for coordination of PRDs is creating a balance or equilibrium. In chapter three, a new market-based coordination algorithm for PRDs based on Dantzig-Wolfe decomposition is proposed. The aforementioned method can find an optimal solution (Nash Equilibrium) where no appliance is willing to change their consumption plan. At the same time, as this algorithm progresses, the generation cost for the generation cost and correspondingly, the user payment is decreased. This procedure is continuing until to a point where no PRD is willing to change their behavior since they reached to their global minimum of their payments.
Electrical engineering, Electrical engineering, Computer science, demand response, machine learning, optimzation, power systems, renewable energy, smart grid
98 pages
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