Characterization of Iran electricity market indices with pay-as-bid payment mechanism

Market data analysis in Iran’s electricity market as a market with a pay-as-bid payment mechanism has been considered in this paper. The analysis procedure includes both predictability and correlation analysis of the most important load and price indices. The experimental data from Iran’s electricity market has been employed in its real size which is long enough to take properties such as non-stationarity of the market into account. For predictability, the characteristics of the hourly accepted Weighted Average Price (WAP) as the topmost price index of this market is analyzed. The analysis tools are time series analysis methods such as power spectral density analysis, phase space reconstruction and test of surrogates, the fractional dimension and the slope of integral sums and the recurrence plots. The results indicate a deterministic, un-stationary and seasonal behavior in addition to unstable periodic orbits and even chaotic behavior in WAP time series. These observations imply just short-term predictability of WAP behavior. The interactive behavior of WAP with the hourly required load (RL) is also considered. For this interaction analysis, in addition to the common correlation methods, cross and joint recurrence plot are also employed. The joint behavioral analysis represents an un-stationary mimic correlation between WAP and RL.     

Unit commitment in electricity pool markets

We consider an electricity generator making offers of energy into an electricity pool market over a horizon of several trading periods (typically a single trading day). The generator runs a set of generating units with given start-up costs, shut-down costs and operating ranges. At the start of each trading period the generator must submit to the pool system operator a new supply curve defining quantities of offered energy and the prices at which it wants these dispatched. The amount of dispatch depends on the supply curve offered along with the offers of the other generators and market demand, both of which are random, but do not change in response to the actions of the generator we consider. After dispatch the generator determines which units to run in the current trading period to meet the dispatch. The generator seeks a supply function that maximizes its expected profit. We describe an optimization procedure based on dynamic programming that can be used to construct optimal offers in successive time periods over a fixed planning horizon.     

Weekly self-scheduling,forward contracting,and pool involvement for an electricity producer. An adaptive robust optimization approach

This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.     

Development and computational tests of an undecoupled optimum short-term hydro-thermal schedulling code using network flows

Summary Optimizing the thermal production of electricity in the short term in an integrated power system when a thermal unit commitment has been decided means coordinating hydro and thermal generation in order to obtain the minimum thermal generation costs over the time period under study. Fundamental constraints to be satisfied are the covering of each hourly load and satisfaction of spinning reserve requirements. A nonlinear network flow model with linear side constraints with no decomposition into hydro and thermal subproblems was used to solve the hydrothermal scheduling. Hydrogeneration is linearized with respect to network variables and a novel thermal generation network is introduced. Computational results are reported.     

微囊藻毒素分子印迹传感器的制备与应用

以邻氨基酚为单体,微囊藻毒素(MC-LR)为模板,采用循环伏安法在金电极的表面电聚合成膜分子印迹材料,制备了传感器。采用安培法对MC-LR进行检测。在制备影响条件最佳值(pH=4.5;单体/模板=1.4×108∶1;洗脱时间10 min)的基础上,对该传感器的线性范围、使用寿命、选择性等进行了研究,并与液相色谱方法进行对比,结果表明:该传感器对MC-LR具有良好的选择性和灵敏度,线性范围为0.05～0.35 mg/L;加标回收率为80%～105%;检出限为7.3μg/L。与液相色谱方法对比,当置信度为99%时,无系统误差。     

Short-run electricity load forecasting with combinations of stationary wavelet transforms

Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.     

Occupation times of the Ornstein–Uhlenbeck process: Functional PCA and evidence from electricity prices

We discuss the functional principal component analysis (FPCA) of the occupation times of the Ornstein–Uhlenbeck process. For the eigenvalue problem of the covariance operator of the occupation times we derive the corresponding integral equation in the large time limit and we solve numerically for the principal components. The formulation applies the path-integral approach of Feynman and Kac. The principal components are compared with those from empirical electricity price processes on energy markets. The results indicate that FPCA of the occupation times is a suitable tool in stochastic energy modeling to generate moderately-sized scenario trees.     

Multistage stochastic portfolio optimisation in deregulated electricity markets using linear decision rules

The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customers’ electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we apply two approximations: we aggregate the decision stages and solve the resulting problem in linear decision rules (LDR). The LDR approach consists of restricting the set of recourse decisions to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.     

Electricity market equilibrium under information asymmetry

We study a competitive electricity market equilibrium with two trading stages, day-ahead and real-time. The welfare of each market agent is exposed to uncertainty (here from renewable energy production), while agent information on the probability distribution of this uncertainty is not identical at the day-ahead stage. We show a high sensitivity of the equilibrium solution to the level of information asymmetry and demonstrate economic, operational, and computational value for the system stemming from potential information sharing.     

Hydrodynamic cavitation-assisted preparation of porous carbon from garlic peels for supercapacitors

Hydrodynamic cavitation (HC), which can effectively induce sonochemical effects, is widely considered a promising process intensification technology. In the present study, HC was successfully utilized to intensify the alkali activation of GPs for SCs, for the first time. Five BDCMs were synthesized following the method reported in the literature. For comparison, four more BDCMs with HC-treated, among which a sample was further doped with nitrogen during the HC treatment, were prepared. Then all the samples were compared from microscopical characteristics to electrochemical performance as SCs materials. The morphology study demonstrated that the HC treatment had created many defects and amorphous carbon structures on the GP-based BDCMs, with the highest SSA reaching 3272 m²/g (1:6-HCGP), which 32 folded that of the Raw carbon sample’s. The HC treatment also intensified the N-doping process. XRD and XPS results manifested that the N content had been increased and consequently changed the electronic structure of the carbon atoms, leading to the increase of specific capacitance (1:6-HCGP+N-based SC, 227 F/g at 10 A/g). The cycle performance proved that the GP-based BDCMs have long-term stability, indicating that the HC-treated BDCMs were good choices for energy storage technologies. Compared with the ultrasound-assisted method, which may have a high energy density, the HC-assisted method enables high production and energy efficiency. This work is a first time attempt towards the industrial application of HC method in energy-related materials synthesis and encourages more in-depth studies in the future.