Dynamics of electricity market correlations

Electricity market participants rely on demand and price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. However, forecasting is hampered by the non-linear and stochastic nature of price time series. Diverse modeling strategies, from neural networks to traditional transfer functions, have been explored. These approaches are based on the assumption that price series contain correlations that can be exploited for model-based prediction purposes. While many works have been devoted to the demand and price modeling, a limited number of reports on the nature and dynamics of electricity market correlations are available. This paper uses detrended fluctuation analysis to study correlations in the demand and price time series and takes the Australian market as a case study. The results show the existence of correlations in both demand and prices over three orders of magnitude in time ranging from hours to months. However, the Hurst exponent is not constant over time, and its time evolution was computed over a subsample moving window of 250 observations. The computations, also made for two Canadian markets, show that the correlations present important fluctuations over a seasonal one-year cycle. Interestingly, non-linearities (measured in terms of a multifractality index) and reduced price predictability are found for the June-July periods, while the converse behavior is displayed during the December-January period. In terms of forecasting models, our results suggest that non-linear recursive models should be considered for accurate day-ahead price estimation. On the other hand, linear models seem to suffice for demand forecasting purposes.     

Bidding strategy with forecast technology based on support vector machine in the electricity market

The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection risk is defined. The other takes into account the impact of the producer’s own bid. The risks associated with the bidding are controlled by the parameter settings. The proposed approaches have been tested on a numerical example.     

Microwave pyrolysis of straw bale and energy balance analysis

The compressed wheat and corn straw bale were pyrolyzed on a microwave heating device self-designed and built with respect to the time-resolved temperature distribution, mass loss and product properties. Considering scale up and technology promotion of microwave pyrolysis (MWP), the investigations on electricity consumption and energy balance of MWP were carried out emphatically. The results indicated that MWP had obvious advantages over conventional pyrolysis, such as heating rapid and more valuable products obtained. The distribution of pyrolysis products such as gas, liquid and char was close to 1:1:1 due to the medium pyrolysis temperature and the slow heating rate, which was not favorable for the formation of gas and/or liquid products. The content of H₂ attained the highest value of 35 vol.% and syngas (H₂ and CO) was greater than 50 vol.%. The electricity consumption of MWP was between 0.58 and 0.65 kW h (kg straw)⁻¹ and with the increase of microwave power, the electricity consumption required for pyrolysis of unit mass of straw increased. The minimum microwave power for MWP was about 0.371 kW (kg straw)⁻¹ and the proportion of heat loss and conversion loss of electricity to microwave energy occupied in the total input energy was 42%. Data and information obtained are useful for the design and operation of pyrolysis of large-sized biomass via microwave heating technology.     

树枝状纳米银颗粒的制备与电活性

在Ag(NH3)2+溶液中,在钛基体上电沉积出树枝状纳米银颗粒,研究了沉积电位对树枝状纳米银颗粒形成的影响,探讨了这种树枝状纳米银颗粒形成的机理,并研究了这种钛基树枝状纳米银电极(Ag/Ti)在碱性溶液中对甲醛氧化的电催化活性。结果表明,在30 mmol/LAg(NH3)2+以及沉积电位在-1.8～-1.2 V(vsAg)时,形成了形态为树枝状的纳米银颗粒。在沉积电位为-1.6 V(vs Ag),Ag(NH3)2+浓度为30 mmol/L的溶液中,电沉积制备的这种树枝状纳米银电极(Ag/Ti)对甲醛氧化具有强的电催化活性。循环伏安曲线表明,在0.1 mol/LNaOH溶液中以及甲醛的浓度范围在0～40 mmol/L,甲醛浓度和它的氧化峰电流密度呈现良好的线性关系,检测下限达到0.662 mmol/L,这种新型的树枝状纳米银电极有望作为甲醛检测的传感器。     

Multicommodity long-term hydrogeneration optimization with capacity and energy constraints

The work presented deals with long-term hydrogeneration optimization in integrated systems when there are no limitations on the availability of fuels for thermal units. A multicommondity network model represents hydrovariables. Hydrogeneration and its unavailability distribution is modeled as a multiblock distribution and a procedure is derived to convolve the hydropower unavailability distribution with the load duration curve. A suitable approximation of the expected production cost is minimized subject to multicommodity network constraints and to hydropower capacity and hydroenergy limit nonlinear constraints. It can be applied to systems with hydrogeneration regardless of its proportion of hydrothermal mix. A realistic case example is solved and the results are discussed. Simulation tests performed with many inflow sequences validate the results obtained.     

A numerical algorithm for pricing electricity derivatives for jump-diffusion processes based on continuous time lattices

We present a numerical algorithm for pricing derivatives on electricity prices. The algorithm is based on approximating the generator of the underlying price process on a lattice of prices, resulting in an approximation of the stochastic process by a continuous time Markov chain. We numerically study the rate of convergence of the algorithm for the case of the Merton jump-diffusion model and apply the algorithm to calculate prices and sensitivities of both European and Bermudan electricity derivatives when the underlying price follows a stochastic process which exhibits both fast mean-reversion and jumps of large magnitude.     

The effect of sample size on the grey system model

Some forecasting models have been developed, each has its own application condition. The grey model is used for small sample forecasting, but until now there is no reasonable explanation for the reason why it is not used for large sample. Therefore, in this paper, matrix perturbation theory is employed to explain the reason. The results of practical numerical examples from previous works demonstrate that the small sample usually has more accuracy than the large sample when establishing grey model in theory. Furthermore, we used the grey model with small samples to analyse the trend of syphilis incidence in China.     

Bilevel optimization applied to strategic pricing in competitive electricity markets

In this paper, we present a bilevel programming formulation for the problem of strategic bidding under uncertainty in a wholesale energy market (WEM), where the economic remuneration of each generator depends on the ability of its own management to submit price and quantity bids. The leader of the bilevel problem consists of one among a group of competing generators and the follower is the electric system operator. The capability of the agent represented by the leader to affect the market price is considered by the model. We propose two solution approaches for this non-convex problem. The first one is a heuristic procedure whose efficiency is confirmed through comparisons with the optimal solutions for some instances of the problem. These optimal solutions are obtained by the second approach proposed, which consists of a mixed integer reformulation of the bilevel model. The heuristic proposed is also compared to standard solvers for nonlinearly constrained optimization problems. The application of the procedures is illustrated in case studies with configurations derived from the Brazilian power system.     

Multi-layer model of correlated energy prices

In this article we develop an extension of the affine jump-diffusion modeling framework and use it to build an intuitive and tractable model of an energy price complex. The development is motivated by the need to model prices of electricity while capturing their dependence on the price of other energy commodities. Such a model is essential for valuing a range of typical derivatives traded in the electricity markets: cross-commodity spread options, cross-location spread options, fuel-switching powerplants, etc. We give an approximate pricing method for these derivatives together with precise error bound estimates.     

Strategic bidding in continuous electricity auctions: an application to the Spanish electricity market

In this paper we introduce an asymmetric model of continuous electricity auctions with limited production capacity and bounded supply functions. The strategic bidding is studied with this model by means of an electricity market game. We prove that for every electricity market game with continuous cost functions a mixed-strategy Nash equilibrium always exists. In particular, we focus on the behavior of producers in the Spanish electricity market. We consider a very simple form for the Spanish electricity market: an oligopoly consisting just of independent hydro-electric power production units in a single wet period. We show that a pure-strategy Nash equilibrium for the Spanish electricity market game always exists.