Demand response plays an important role in the development of the smart grid, which can effectively manage society's energy consumption. Cooling devices, such as refrigerators and freezers, are ideal devices for demand-response programs because their energy states can be controlled without reducing the lifestyle and comfort of the residents. Conversely, managing air conditioning and space heating would affect a resident's comfort level. Direct compressor control and thermostat control methods have been proposed in the past for controlling cooling devices but they are never studied concurrently. This paper proposes a new control mechanism and compares the effectiveness of the three control mechanisms for cooling devices in demand response. In addition, this paper illustrates the need for a damping strategy to mitigate demand oscillations that occur from synchronous fleet control.

In this paper, the value of intra-day markets in managing wind power uncertainty in competitive electricity markets is analyzed. A competitive electricity market model consisting of a day-ahead market and a number of intra-day markets is considered. Real-time operation adjustment is also taken into account. Stochastic programming is used to model decision making under wind power uncertainty. Numerical simulations based on two test systems are presented.

In this paper, we propose a unified framework for inclusion of different electricity tariff components and residential prosumption devices in a residential energy management system (REMS). This framework allows REMS to use flexible modelling with respect to devices and price components applicable to an individual house, without requiring unique models for each device and tariff. The significance of this work is that the proposed framework facilitates the process for investigating how dwellings may respond to various tariffs when energy prosumption is optimally scheduled. In this work, we include six cost components and four response classes to illustrate the usage of this framework.

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for residential energy management systems (REMS) in demand response market. Several techniques are available for optimizing the operation schedules and decisions of REMS. However, it can be challenging for REMS to capture sufficient resolution and horizon to make good short-term and long-term decisions, respectively, under limited computing resources. We propose a two-horizon algorithm, which can achieve high resolution schedules with reasonable value of energy services while limiting computation time. Simulation results are provided to confirm the validity of the proposed approach.

Residential energy management is an important research topic due to the opportunities offered by the coming of Smart Grid, and increasing concern of greenhouse gas emissions. This works demonstrates the formulation and use of optimization to improve energy consumption and production schedules for a single dwelling, and how to integrate it using a moving window error correcting algorithm. The presented numerical results demonstrate that the proposed algorithm outperforms a baseline model without the mowing window algorithm, and is more robust against forecast errors and fluctuations beyond the scope of the scheduling period.

Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize specific price thresholds as the basis for making short-term scheduling decisions. In this paper, classification of future electricity market prices with respect to pre-specified price thresholds is introduced. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for classifying prices in Ontario's and Alberta's markets.

This paper introduces two extenstions to the Cumulant Method (CM) for probabilistic optimal power flow (P-OPF) studies; the first is an enhancement to provide improved handling of limits within the P-OPF problems and the second is a way to include correlated variables. The first enhancement is termed the Limit Corrected Cumulant Method (LCCM) which specifically addresses errors in the existing CM when limits, away from the mean solution, are encountered while solving a P-OPF problem. The LCCM approach relies on the CM to produce multiple probability density functions (PDFs) and then combines these PDFs together to generate final PDFs. The second enhancement for incorporating correlated variables into P-OPF problems is based on the composition of correlated variables from several independent ones. The proposed approaches are verified against Monte Carlo simulations (MCS) consisting of 10 000 samples and demonstrate significant improvements when compared with traditional CM results.

This paper presents the comparison of two solution methods for probabilistic optimal power flow problems; namely, the two-point estimate method (2PEM) and the cumulant method (CM). The goal of the P-OPF problem is to determine the probability distributions for all random variables in the problem. In this paper, bus loading and generators' supply power bids are considered as uncertain or probabilistic parameters in a P-OPF problem. Due to their importance in the context of electricity markets, special attention is paid to the uncertainty in locational marginal prices (LMPs) that results from uncertain behavior of market players. The proposed methods are tested on a modified version of the Matpower 30-bus system to demonstrate the capabilities of both approaches. Solution methodologies are compared in terms of accuracy and computational burden. Results are compared against those obtained from 10,000 sample Monte Carlo simulations (MCS). The proposed methods show high accuracy levels and are computationally significantly faster than an MCS approach

This paper proposes a mathematical program with complementarity constraints to better model the relationship between the base, or current, operating point and the maximum loading point in a power system when solving maximum loading problems.

This paper proposes a Cumulant Method-based solution to solve a maximum loading problem incorporating a constraint on the maximum variance of the loading parameter. The proposed method takes advantage of some properties regarding saddle node bifurcations to create a linear mapping relationship between random bus loading variables and all other system variables.The proposed methodology is tested using a sample system based on the IEEE 30-bus system using random active and reactive bus loading. Monte Carlo simulations consisting of 10000 samples are used as a reference solution for evaluation of the accuracy of the proposed method.

The application of market based approaches to power systems has, in general, resulted in the reduction of stability margins as profit maximization can lead to systems being operated in stressed conditions. As systems are operated closer to their limits, it is critical that the system is modeled appropriately and that control actions take into account stability margins. This paper reviews three recent tools for power system dynamic performance; first a probabilistic optimal power flow (P-OPF), which is used to incorporate uncertainty in system modeling; second, complementarity modeling is reviewed, as this approach allows for more appropriate modeling of how the system moves from stable equilibrium to unstable or loss of equilibrium. Finally, the normal boundary intersection method is presented. This method allows one to form the Pareto surface efficiently when considering a multi-objective optimization problem, such as a stability constrained optimal power flow

Load control and demand side load management programs have been implemented in a large number of competitive power markets. These programs can provide enhanced system security and many benefits to participants. This paper reviews and compares existing economically driven programs.

This paper introduces the cumulant method for the P-OPF problem and compares the cumulant method and first-order second-moment method (FOSMM). It is shown that for some combinations of independent Gaussian distributions the two methods are equivalent. A numerical example is included comparing the cumulant method and FOSMM results with a 1500 Monte Carlo simulation. The results using the FOSMM exactly equal the results using the cumulant method.

This paper presents a cumulant method based solution for stochastic optimal power flow (S-OPF) including a variance minimization objective function. In this case, the objective of the problem is to minimize the variance of the active power generation at the slack bus by optimizing the decision variables given uncertainty in loading levels. Results using the proposed method are compared against Monte Carlo simulations to demonstrate accuracy of the results. Additionally, variances computed at the optimal point are compared against the variances computed at the minimum cost point to provide a measurement of the improvement by the optimization process. Two systems are tested consisting of 30 and 57 buses respectively. In general, variances computed using the proposed methodology are within 1% of those computed using 10,000 sample Monte Carlo simulations and the variances are between 3 and 11% narrower than those at the minimum cost solution.

The paper presents a method for decentralized coordination of inter-regional static stability management. This approach allows for an optimal "central" result using a decentralized coordinated solution procedure. The proposed optimization-based approach achieves system-wide efficiency of inter-regional electricity systems or markets while minimizing the amount of information that needs be exchanged. The solution is obtained by adapting decentralized solution techniques of interior point methods to the static stability problem.

This paper presents a stochastic non-linear program (S-NLP) with a confidence interval constraint. The problem extends the conventional maximum loading problem to include randomness and uncertainty in system loading levels. The problem restricts the 99% confidence interval of the loading level to be within a pre-specified amount of the mean. The paper presents solutions when the confidence interval is restricted to be within 15, 20, and 25% of the mean. The proposed solution methodology is tested using the IEEE 30 bus system and results are compared against solutions found using Monte Carlo simulations. Each of the Monte Carlo simulations consist of 10,000 samples.

This paper introduces the cumulant method for the probabilistic optimal power flow (P-OPF) problem. By noting that the inverse of the Hessian used in the logarithmic barrier interior point can be used as a linear mapping, cumulants can be computed for unknown system variables. Results using the proposed cumulant method are compared against results from Monte Carlo simulations (MCSs) based on a small test system. The Numerical Results section is broken into two sections: The first uses Gaussian distributions to model system loading levels, and cumulant method results are compared against four MCSs. Three of the MCSs use 1500 samples, while the fourth uses 20 000 samples. The second section models the loads with a Gamma distribution. Results from the proposed technique are compared against a 1000-point MCS. The cumulant method agrees very closely with the MCS results when the mean value for variables is considered. In addition, the proposed method has significantly reduced computational expense while maintaining accuracy.