In 2016, the first batch of concentrated solar power (CSP) demonstration projects of China was formally approved. Due to the important impact of the cost-benefit on the investment decisions and policy-making, this paper adopted the static payback period (SP), net present value (NPV), net present value rate (NPVR), and internal rate of return (IRR) to analyze and discuss the costbenefit of CSP demonstration plants. The results showed the following.(1) The SP of CSP systems is relatively longer, due to high initial investment; but the cost-benefit of CSP demonstration plants as a whole is better, because of good expected incomes. (2) Vast majority of CSP projects could gain excess returns, on the basis of meeting the profitability required by the benchmark yield of 10%. (3) The cost-benefit of solar tower CSP technology (IRR of 12.33%) is better than that of parabolic trough CSP technology (IRR of 11.72%) and linear Fresnel CSP technology (IRR of 11.43%). (4) The annual electricity production and initial costs have significant impacts on the cost-benefit of CSP systems; the effects of operation and maintenance costs and loan interest rate on the cost-benefit of CSP systems are relatively smaller but cannot be ignored.

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach.

Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorithm (FOA), and least square support vector machine (LSSVM) model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA), and empirical mode decomposition (EMD)-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.

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