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The robust generation self-scheduling problem under electricity price uncertainty is usually solved by the commercial solver, which is limited in computation time and memory requirement. This paper proposes an improved Lagrangian relaxation algorithm for the robust generation self-scheduling problem where the quadratic fuel cost and the time-dependent exponential startup cost are considered. By using the optimal duality theory, the robust generation self-scheduling problem, which has a max-min structure, is reformulated as a minimization mixed integer nonlinear programming (MINLP) problem. Upon the reformulation, the Lagrangian relaxation algorithm is developed. To obtain a solvable relaxed problem, the variable splitting technique is introduced before the relaxation. The obtained relaxed problem is decomposed into a linear programming-type subproblem and multiple single-unit subproblems. Each single-unit subproblem is solved optimally by a two-stage backward dynamic programming procedure. The special cases of the problem are discussed and a two-stage algorithm is proposed. The proposed algorithms are tested on test cases of different sizes and the numerical results show that the algorithms can find near-optimal solutions in a reasonable time. [ABSTRACT FROM AUTHOR]

Summary: Integrating variable renewable energy from wind farms into power grids presents challenges for system operation, control, and stability due to the intermittent nature of wind power. One of the most promising solutions is the use of compressed air energy storage (CAES). The main purpose of this paper is to examine the technical and economic potential for use of CAES systems in the grid integration. To carry out this study, 2 CAES plant configurations: adiabatic CAES (A‐CAES) and diabatic CAES (D‐CAES) were modelled and simulated by using the process simulation software ECLIPSE. The nominal compression and power generation of both systems were given at 100 and 140 MWe, respectively. Technical results showed that the overall energy efficiency of the A‐CAES was 65.6%, considerably better than that of the D‐CAES at 54.2%. However, it could be seen in the economic analysis that the breakeven electricity selling price (BESP) of the A‐CAES system was much higher than that of the D‐CAES system at €144/MWh and €91/MWh, respectively. In order to compete with large‐scale fossil fuel power plants, we found that a CO2 taxation scheme (with an assumed CO2‐tax of €20/tonne) improved the economic performance of both CAES systems significantly. This advantage is maximised if the CAES systems use low carbon electricity during its compression cycle, either through access to special tariffs at times of low carbon intensity on the grid, or by direct coupling to a clean energy source, for example a 100‐MW class wind farm. [ABSTRACT FROM AUTHOR]

An Internal Reforming Solid Oxide Fuel Cell (IRSOFC) is modeled thermodynamically; a Horizontal Axis Wind Turbine (HAWT) is designed; the combined IRSOFC-HAWT system should create a reliable source of electricity for the demand of a village located in Manjil, Iran. The output power of HAWT is unstable, but by controlling the fuel rate for the IRSOFC it is possible to have a stable power output from the combined system. When the electricity demand is over the peak or the wind speed is low/unstable/significantly high, the generated power may not be sufficient. To solve this problem, two scenarios are considered: connecting to the grid or using a Pumped Hydro Storage (PHS). For the second scenario, the extra produced electricity is saved when the production is more than demand and can be used when the extra power is needed. The economic analysis is done based on the economic conditions in Iran. The results will show a period of return about 9.5 and 13 years with the levelized cost of electricity about 0.0747 and 0.0882 $/kWh for the first and second scenarios, respectively. Furthermore, effects of some parameters such as the electricity price and the real interest rate are discussed. [ABSTRACT FROM AUTHOR]

The objective of the retailer in medium-term planning is managing the portfolio of contracts from different sources as well as determining the optimal selling price offered to its customers. When supplying the electricity sold to the costumers, two main challenges are faced by retailers. The first problem occurs during the electricity procurement procedure. In this stage, the retailer must deal with the uncertainty due to the pool price that propels the retailers to move towards agreeing to forward contracts signed at higher average prices. Besides, when the retailer decides on selling the electricity, another problem is to face the uncertainty caused by the demand while taking into consideration the possibility of reducing its clients in the case of high selling price. In this regard, this paper proposes a stochastic multi-objective framework for the retailer with profit maximization and risk minimization as two objective functions. The risk, due to the market price uncertainty, is modeled, employing the expected downside risk. The problem is formulated as mixed-integer programming while the stochastic optimization problem is characterized using the roulette wheel mechanism and lattice Monte Carlo simulation. Furthermore, lexicographic optimization and augmented epsilon-constraint method are used to solve the proposed multi-objective problem, and the best compromise solution is determined employing a fuzzy satisfying method. The presented model has been implemented using a realistic case study to verify the effectiveness of the method used in this paper. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called "chaos particles optimization (CPSO) weight-determined combination models." These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models. [ABSTRACT FROM AUTHOR]

SUMMARY This paper develops a new approach to fairly clear the market and assess the amount of each participant's revenue in a deregulated power system, through modifying the system nodal prices. This approach equalizes the economic profit of each participant group to its fair and rational value. Transco behavior is modeled as an independent entity without direct supervision of an independent system operator, causing the possibility of either making profit or incurring a loss for it. With this method applied, three main results for better system design and operation are produced: The rational and acceptable profit/loss values for all participant groups (producers, customers, and Transco) are determined through fair apportionment of total system profit among them based on their optimality and contribution of profitability to the whole system., The profit/loss value for each of producers and consumers in the system is allocated according to their proportional efficiency and extent of transmission use., Transmission revenue assessment is performed through defining a criterion for evaluating the transmission network optimality., So this approach can correct wrong and unfair economic signals in the previous electricity pricing and participants' revenue assessment methods, presenting such a fairer mechanism for it that each participant's action will be oriented in maximizing the system's total economic efficiency. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a new approach to coordinate the decisions of transmission and generation capacity expansion planning for a competitive electricity market in which only the generation sector is deregulated. The independent system operator (ISO) as transmission planner has a regulatory role in the strategic behavior of generation companies. To reach coordinated decisions, the model relies on an interactive and iterative process between generation expansion planning and transmission expansion planning. This repetitive process continues until reaching a converging point or fulfilling the stopping criterion assigned by the ISO. In order to consider random outages of network components as well as uncertainty of load and bid prices of generating units, the Monte Carlo simulation method is applied. The simulation results confirm the efficacy of the proposed model in the coordinated generation and transmission planning problem considering the uncertainties. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]