Allocating electricity transmission costs through tracing: a game-theoretic rationale

Tracing is a method of assigning flows in an electricity network to particular generators and loads, assuming perfect mixing at each node. It can be used to assign costs to transmission users. We show that the resulting allocation is equal to the Shapley value of an equivalent co-operative game.     

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.     

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.     

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.     

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.     

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.     

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

In energy systems with high shares of weather-driven renewable power sources, gas-fired power plants can serve as a back-up technology to ensure security of supply and provide short-term flexibility. Therefore, a tighter coordination between electricity and natural gas networks is foreseen. In this work, we examine different levels of coordination in terms of system integration and time coupling of trading floors. We propose an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming. This formulation co-optimizes day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost. Additionally, two deterministic models, one of an integrated energy system and one that treats the two systems independently, are presented. We utilize a formulation that considers the linepack of the natural gas system, while it results in a tractable mixed-integer linear programming (MILP) model. Our analysis demonstrates the effectiveness of the proposed model in accommodating high shares of renewables and the importance of proper natural gas system modeling in short-term operations to reveal valuable flexibility of the natural gas system. Moreover, we identify the coordination parameters between the two markets and show their impact on the system’s operation and dispatch.     

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.     

A jump diffusion model for spot electricity prices and market price of risk

We construct a jump-diffusion model with seasonality, mean-reversion, time-dependent jump intensity and heteroskedastic disturbance for electricity spot prices, while keeping the analytical tractability of futures prices. We find that the jump component plays a considerably larger role than the diffusion component in the variance of spot prices. Moreover, the jump intensity is much higher during summer and winter. We also explore the seasonal market price of risk (MPR) with different maturities, from one month to five months. Our results show that the diffusion risk and the jump risk are priced quite differently.     

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.