• Energy Research

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

The smart distribution system architecture provides value‐based control techniques that facilitate bi‐directional power flows and energy transactions. Although consensus and understanding continue to develop around peer‐to‐peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two‐stage of power scheduling. The proposed model is verified on the modified IEEE 15‐bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk‐seeking operation strategy is recommended if wind power penetration increases.

In a carbon-constrained world, the continuing and rapid growth of gas-fired power generation (GPG) will lead to the increasing demand for natural gas. The reliable and affordable gas supply hence becomes an important factor to consider in power system planning. Meanwhile, the installation of GPG units should take into account not only the fuel supply constraints but also the capability of sending out the generated power. In this paper, a novel expansion co-planning (ECP) model is proposed, aiming to minimize the overall capital and operational costs for the coupled gas and power systems. Moreover, linear formulations are introduced to deal with the nonlinear nature of the objective functions and constraints. Furthermore, the physical and economic interactions between the two systems are simulated by an iterative process. The proposed linear co-planning approach is tested on a simple six-bus power system with a seven-node gas system and a modified IEEE 118-bus system with a 14-node gas system. Numerical results have demonstrated that our co-planning approach can allow systematic investigations on supporting cost-effective operating and planning decisions for power systems.

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy.

Abstract In this paper, we present a framework that aims to minimise power generation costs while satisfying carbon emission constraints. We then construct a carbon-abatement investment-option game model for asymmetric generation companies. The results show that the investment behavior of generation companies is largely affected by the critical value of the electricity price after carbon-abatement investment (relative decarbonization price). When the relative decarbonization price is lower than the critical value, only generation companies with low emission are motivated to invest in carbon abatement. By contrast, companies with high emission will only make such investments when the relative decarbonization price is higher than the critical value. We find that raising the proportion of competitive electricity will stimulate generation companies to invest in carbon abatement, while the influence of carbon-emission targets on investment behavior is uncertain. Compared with implementing a subsidy, increasing the relative decarbonization price can motivate generation companies to invest in carbon abatement more effectively.