Chuanwen Jiang

The Formulation of the Optimal Strategies for the Electricity Producers Based on the Particle Swarm Optimization Algorithm

In competitive electricity markets, the producer as a market participant strives to find the optimal supply function with the objective of maximizing his/her producer surplus in the market clearing. The model of the producer surplus maximization is a bilevel mathematical programming problem within which the market clearing is taken into account. By using the deterministic approaches, it is difficult to obtain the global solution of the bilevel optimization problem, even for a single hourly market clearing. This is due to the fact that the objective function of such a problem is not concave, and there are nonlinear complementarity terms introduced by using the KKT conditions to represent the market clearing. When the bilevel optimization problem is modified to consider multiple hourly market clearings, such as to maximize the total producer surplus in one day, solving such a problem is almost intractable. A heuristic approach should be another option. For its simplicity and immunity to the local optimum, the particle swarm optimization (PSO) algorithm is employed in this paper to find the optimal supply function of the electricity producer. Based on the IEEE 30-bus test system, different simulation cases with respect to a single hourly market clearing and a daily market clearing are tested to show the efficiency and robustness of the PSO algorithm. In addition, the parameterization techniques used in formulating the optimal supply function are analyzed based on the simulation results

Probability models for estimating the probabilities of cascading outages in high-voltage transmission network

This paper discusses a number of probability models for multiple transmission line outages in power systems, including generalized Poisson model, negative binomial model, and exponentially accelerated model. These models are applied to the multiple transmission outage data for a 20-year period for North America. The probabilities of the propagation of transmission cascading outage are calculated. These probability magnitudes can serve as indexes for long-term planning and can also be used in short-term operational defense to such events. Results from our research show that all three models apparently explain the occurrence probability of higher order outages very well. However, the exponentially accelerated model fits the observed data and predicts the acceleration trends best. Strict chi-squared fitness tests were done to compare the fitness among these three models, and the test results are consistent with what we observe

Short-Term Operation Model and Risk Management for Wind Power Penetrated System in Electricity Market

In view of the uncertainty and intermittency of wind power, this paper proposes an optimal economical dispatch (ED) model and develops a method to estimate risk and manage hybrid power systems (traditional + wind power systems) for the short-term (24 h) operations. The model and the method have taken into account the large wind power penetration and the wind variability. The particle swarm optimization (PSO) algorithm with constraints is applied to solve the ED problem. Value at risk (VaR) and integrated risk management (IRM) are used separately to assess the risk, so that an optimal tradeoff between the profit and risk is made for the system operations. The model and the method are tested on the standard IEEE 30-bus power system and network in Shanghai. The validity of the model and the method has been approved.

Regional Carbon Emission Management Based on Probabilistic Power Flow With Correlated Stochastic Variables

Most existing carbon emission management strategies only control the total carbon emission without focusing on both the regional carbon emission and the stochastic properties of the system. Correlated regional loads and unpredictable renewable energies in the power system make regional carbon emission management (RCEM) increasingly challenging and necessary. A complex multi-objective RCEM model based on probabilistic power flow (PPF) considering correlated variables is contributed in this paper. The three objective functions to be minimized are 1) the cost of electricity generated, 2) the total carbon emission, and 3) the carbon emission difference among regions which reflects the regional carbon emission imbalance from the supply side. A new clonal selection algorithm (CSA) coupled with a fuzzy satisfying decision method and an extended 2 m +1 point estimate method (PEM) is proposed to solve this multi-objective RCEM model. The proposed method is illustrated through IEEE 30-bus, IEEE 118-bus and simplified Shanghai case studies. The proposed model can help reduce the total carbon emission, control regional carbon emission, prevent probabilistic congested lines from overloading, and choose the most suitable region for wind farms (WFs).