Energy Consumption Scheduling of HVAC Considering Weather Forecast Error Through the Distributionally Robust Approach

In this paper, the distributionally robust optimization approach (DROA) is proposed to schedule the energy consumption of the heating, ventilation and air conditioning (HVAC) system with consideration of the weather forecast error. The maximum interval of the outdoor temperature is partitioned into subintervals, and the proposed DROA constructs the ambiguity set of the probability distribution of the outdoor temperature based on the probabilistic information of these subintervals of historical weather data. The actual energy consumption will be adjusted according to the forecast error and the scheduled consumption in real time. The energy consumption scheduling of HVAC through the proposed DROA is formulated as a nonlinear problem with distributionally robust chance constraints. These constraints are reformulated to be linear and then the problem is solved via linear programming. Compared with the method that takes into account the weather forecast error based on the mean and the variance of historical data, simulation results demonstrate that the proposed DROA effectively reduces the electricity cost with less computation time, and the electricity cost is reduced compared with the traditional robust method

Managing power system congestion and residential demand response considering uncertainty

Electric power grids are becoming increasingly stressed due to political and environmental difficulties in upgrading transmission capacity. This challenge receives even more interest with the paradigm change of increasing renewable energy sources and demand response (DR) programs. Among DR technologies, existing DR programs are primarily designed for industrial and commercial customers. However, household energy consumption accounts for 38% of total electricity consumption in the U.S., suggesting a significant missed opportunity. This dissertation presents an in-depth study to investigate managing power system congestion and residential DR program under uncertainty.First, an interval optimization model is presented for available transfer capability (ATC) evaluation under uncertainties. The conventional approaches of ATC assessment include deterministic and probabilistic methods. However, the proposed interval optimization model can effectively reduce the accuracy requirements on the renewable forecasting, and lead to acceptable interval results by mitigating the impacts of wind forecasting and modeling errors. Second, a distributed and scalable residential DR program is proposed for reducing the peak load at the utility level. The proposed control approach has the following features: 1) it has a distributed control scheme with limited data exchange among agents to ensure scalability and data privacy, and 2) it reduces the utility peak load and customers’ electricity bills while considering household temperature dynamics and network flow.Third, the impacts of weather and customers’ behavior uncertainties on residential DR are also studied in this dissertation. A new stochastic programming-alternating direction method of multipliers (SP-ADMM) algorithm is proposed to solve problems related to weather and uncertain customer behavior. The case study suggests that the performance of residential DR programs can be further improved by considering these stochastic parameters.Finally, a deep deterministic policy gradient-based (DDPG-based) HVAC control strategy is presented for residential DR programs. Simulation results demonstrate that the DDPG-based approach can considerably reduce system peak load, and it requires much less input information than the model-based methods. Also, it only takes each agent less than 3 seconds to make HVAC control actions. Therefore, the proposed approach is applicable to online controls or the cases where accurate building models or weather forecast information are not available

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework