Donghan Feng

Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles

Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.

Reactive Market Power Analysis Using Must-Run Indices

This paper investigates the issues of reactive power must-run capacity in power system operations, hence in electricity markets. A must-run index-based method is proposed in the paper to measure the market power held by reactive power suppliers. The Nordic 32-bus system and the IEEE 118-bus system are used to test the proposed method. The market power holders of reactive power found using the proposed method are in accord with that found in the realistic Nordic system operation and in the existing analysis of IEEE 118-bus system. The paper identifies through must-run indices possible conditions that could lead to market power in the case of applying a bid structure within a market framework. Furthermore, market structure drawbacks can cause the appearance of market power even in a topologically ideal system.

Electricity markets and power system economics

Introduction Demand and Supply Market Equilibrium Price Elasticity and Competitive Market Economy of Scale and Natural Monopoly Brief History of Electricity Markets Fundamentals of Power System Operation Economic Dispatch Load Flow Calculation Load Flow under Outages Fundamentals of Constrained Optimization Security-Constrained Economic Dispatch Load Frequency Control Spinning Reserve Generation Scheduling Calculation of Transfer Capabilities of Transmission Interfaces Overview of Power System Operation Market Design: Spot Energy Market Organization after Deregulation Uniform Pricing Nodal Pricing Multiple Block Bidding Demand Side Bidding Day-Ahead Market Ex-Post Spot Pricing Transmission Losses Bilateral Trading in United Kingdom Electricity Market Reform in California Market Design: Procurement of Ancillary Services Reserve Market AGC Market Energy, Reserve, and AGC Co-Optimization Market Compensation without Competition Market Design: Common Cost Allocations Background Transmission Costs Unit Start-Up Cost Peaking Cost Compensation Transmission Rights Microeconomic Analysis Background Fundamentals of Non-Cooperative Game Theory Game Models for Market Analysis Market Power Analysis Electricity Market Experiments Price Forecast and Risk Management Forecasting Electricity Prices Managing Price Risk

Spot Pricing When Lagrange Multipliers Are Not Unique

Classical spot pricing theory is based on multipliers of the primal problem of an optimal market dispatch, i.e., the solution of the dual problem. However, the dual problem of market dispatch may yield multiple solutions. In these circumstances, spot pricing or any standard pricing practice based on multipliers cannot generate a unique clearing price. Although such situations are rare, they can cause significant uncertainties and complexities in market dispatch. In practice, this situation is solved through simple empirical methods, which may cause additional operations or biased allocation. Based on a strict extension of the principles of spot pricing and surplus allocation, we propose a new pricing methodology that can yield unique, impartial, and robust solution. The new method has been analyzed and compared with other pricing approaches in accordance with spot pricing theory. Case studies support the results of the theoretical analysis, and further demonstrate that the method performs effectively in both uniform-pricing and nodal-pricing markets.

New real-time market facilitating demand-side resources for system balancing

Many demand side resources have the potential to provide fast and low cost balancing services. Switching these devices on and off can be executed in seconds and have limited consequences for the customers if the duration is not long. With carefully designed market rules, tens of thousands of such loads can deliver a well-behaved, stable and predictable balancing support. Based on a comprehensive analysis of the current balancing markets, as well as the technical properties of traditional and potential balancing resources, this paper proposes a new real-time balancing market setup facilitating the participation of demand-side resources. In light of the future environment of increasing intermittent renewable power and distributed energy/storage resources, stochastic time-series and Monte-Carlo simulation are used to analyze the relationship between balancing requirement and generation/demand uncertainties. This research is potentially useful for designing future balancing markets and utilizing demand response for system services.

Congestion Surplus Minimization Pricing Solutions When Lagrange Multipliers are not Unique

This paper proposes a comprehensive solution methodology for the pricing difficulty when Lagrange multipliers are not unique. A linear optimization model is proposed to solve the congestion-related pricing difficulty. The objective function of the model is set to be minimizing the congestion surplus. In addition, an incentive-based allocation approach is incorporated in the solution procedure for cases when no marginal participant exists. The unique pricing solution obtained through our methodology can achieve proper reallocation of the undetermined surplus. Further, we discuss the reference bus independence property of the proposed pricing methodology. Numerical results are provided to fully test the proposed methodology. Other possible solutions are also presented for comparison.

Directed graph based carbon flow tracing for demand side carbon obligation allocation

In order to achieve carbon emission abatement, some researchers and policy makers have cast their focus on demand side carbon abatement potentials. This paper addresses the problem of carbon flow calculation in power systems and carbon obligation allocation at demand side. A directed graph based method for tracing carbon flow is proposed. In a lossy network, matrices such as carbon losses, net carbon intensity (NCI) and footprint carbon intensity (FCI) are obtained with the proposed method and used to allocate carbon obligation at demand side. Case studies based on realistic distribution and transmission systems are provided to demonstrate the effectiveness of the proposed method.

Optimal Demand Contracting Strategy Under Uncertainty and Its Implication for Advanced Pricing

Based on the current practice of charging peak demand, an expectation-oriented stochastic model is established for the demand contracting decision of electricity users. The resultant optimization problem is proved to be nonlinear but convex. Thus, the first-order optimality condition of the proposed model, which leads to an integral equation founded on the probability density function of the peak load, is used to derive the optimal contracting strategy for consumers. Simulation results support the convexity of the proposed model and the effectiveness of the proposed solution method under different demand charging rules and different consumption patterns of customers.

Application of carbon intensity in generation expansion planning: A comparative study

Generation expansion planning (GEP), which integrates various environmental constraints under low-carbon economy, is a challenging problem due to its complexity. In this paper, different carbon intensity indexes are introduced to simplify the GEP problem. Marginal carbon intensity (MCI) and footprint carbon intensity (FCI) are mathematically formulated, and their corresponding GEP methods are provided. Based on a modified IEEE RTS 24-bus system, a comparative analysis of these methods in terms of carbon abatement and electricity purchasing cost is presented. Furthermore, the correlations between the methods are given to illustrate the simulation results. Finally, the general characteristics of each method are concluded.

Redesign electricity market for the next generation power system of renewable energy and distributed storage technologies

This paper proposes a stochastic time-series based method to simulate the volatility of intermittent renewable generation and distributed storage devices along timeline. The proposed method can calculate the optimal timeline for different electricity markets and power systems. In practice, the proposed method is potentially useful for designing market rules and evaluating different design options. Following works is underway on application and simulation of proposed method using the realistic distribution system of Bornholm Island in Denmark.