Here we present a simple hot press based method for processing La(Fe,Si)13-based compounds consisting of La-Fe-Co-Si-C particles and phenolic resin. The magnetic entropy change ΔS per unit mass for the LaFe10.87Co0.63Si1.5C0.2/phenolic resin compounds have nearly the same magnitude with the base materials. With the content of phenolic resin of 5.0 wt%, the compound conductivity is 3.13 W m-1 K-1. In order to measure the cooling performance of La(Fe,Si)13-based compounds, the La(Fe11.6-xCox)Si1.4C0.15 (x=0.60, 0.65, 0.75, 0.80, 0.85)/phenolic resin compounds were pressed into thin plates and tested in a hybrid refrigerator that combines the active magnetic refrigeration effect with the Stirling cycle refrigeration effect. The test results showed that maximum cooling power of 41 W was achieved over a temperature span of 30 K.

Energy retailers that sell energy at fixed prices are at risk of bankruptcy due to instantaneous fluctuations in wholesale electricity prices. Energy derivatives, e.g., electricity options, can be purchased by energy retailers then sold to customers as one potential risk-mitigation tool. A class of energy retailers that trade energy derivatives, including the electricity option, the carbon option and the green certificate, is considered in this paper. In terms of energy retailers, a strategy that can maximize the value of the purchased energy derivatives over a period of time and minimize the risk due to the stochastic price fluctuations is developed. Firstly, the dynamic prices of the electricity option as well as the carbon option are described by stochastic differential equations, and the dynamic prices of the green certificate are described by ordinary differential equations. Historical price data are used to obtain the parameters of both stochastic and ordinary differential equations by maximum likelihood estimation. Next, an investment portfolio is established as a mean-variance portfolio selection problem where the retailer maintains the satisfactory asset value and minimizes the risk simultaneously. Then, the problem is transformed into a stochastic optimal control problem which can be solved analytically by using the linear-quadratic method. Finally, the numerical simulations illustrate the feasibility of the proposed method.