Energy Conversion and Economics最新文献

The DC multi-port converter's applications are increasing owing to its favourable features, including variable control mode, high power density, and bi-directional power supply. On the other hand, the impedance interaction between each port of the electrolytic capacitor-less DC multi-port converter may generate an instability. To address the above mentioned problem, a method is proposed in this paper for reshaping the impedances of the energy storage converter by constructing a virtual impedance connected in parallel with the output impedance of the electrolytic capacitor-less DC multi-port converter. Furthermore, this paper introduces the stability criterion based on the unified impedance theory, which classifies a converter as either the bus current-controlled converter or the bus voltage-controlled converter. The proposed control approaches raise the magnitude of the output impedance of the electrolytic capacitor-less DC multi-port converter to satisfy the unified impedance theory criterion without modifying each port. The simulation results show that the proposed method is better than the traditional methods, and comprehensive experimental results are provided to validate the proposed methods.

Green hydrogen production via photovoltaic (PV)-electrolysis is a promising method for addressing global climate change. The battery provides a stable power supply for the PV-electrolysis system. Hence, this study proposes a robust model for configuring the capacity of a PV-battery-electrolysis hybrid system by considering the dynamic efficiency characteristics and cost learning curve effect of key equipments. As a segmented function, the dynamic efficiency of electrolysis is incorporated into the robust model, which describes the hydrogen production efficiency based on power fluctuations. A learning curve model is developed based on historical data from 2012 to 2020 to predict future capital expenditure. Major results are as follows: (1) The use of dynamic efficiency characteristics can reflect the real-time status of the electrolysis more accurately, and make the capacity configuration more reasonable compared with fixed efficiency. (2) Considering the effect of the learning curve, by 2050, the capital expenditure of the PV panel and proton exchange membrane electrolysis can be dropped to 2981 and 1992 CNY/kW, respectively. (3) The optimal case considering uncertainty currently is a 1 MW PV panel equipped with 242 kW electrolysis and 2276 kW battery.

The wide deployment of distributed energy resources, combined with a more proactive demand-side management, is boosting the emergence of the peer-to-peer market. In the present study, an innovative peer-to-peer energy trading model is introduced, enabling a group of price-setting prosumers to engage in direct negotiations via a straightforward best-response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterward, an asynchronous and convergence-fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

In power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open-loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed-loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long-term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization-based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three-layer closed-loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large-scale renewable integration (XJTU-ROTS) demonstrate the effectiveness of our proposed framework.

Singapore's power sector has set targets of net-zero emissions by 2050. Given the limited land space of the country, a key strategy to decarbonize the power grid is to import clean power from renewable energy resources such as photovoltaic (PV) plants installed at overseas locations. The present electricity market rules require such overseas PV plants to maintain constant power generation during each bidding period. To meet such requirements, energy storage systems (ESSs) are to be deployed in the PV plants to compensate for the PV power fluctuation. This paper proposes an optimal power bidding approach for maximizing the profit of the PV plant participating in the Singapore wholesale electricity market. The problem is formulated as a stochastic programming model, which takes the short-term PV power forecasting as the input, maximizes the expected profit considering the PV power selling revenue and the penalty cost for power shortfall during each bidding cycle (30 min), and satisfies constraints of the ESS. The proposed method can also be used for determining the optimal size of the ESS. Simulation results have verified the effectiveness of the proposed method.

The increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low-carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer-to-peer (P2P) electricity and carbon trading in multi-energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value-at-risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision-making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

Power system dynamics are generally modeled by high dimensional non-linear differential-algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non-linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

The expansion of the DC fast-charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast-charging stations. The review is closely aligned with current state-of-the-art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

In recent years, the frequent occurrence of natural disasters has seriously threatened the security and stability of distribution networks. Amidst these natural disasters, researchers have increasingly focused on ensuring fault recovery in distribution networks. Soft open points (SOPs) are new types of power electronic devices that can effectively recover faults in distribution networks. When a fault occurs, SOPs can quickly block and isolate fault short-circuit current, provide power access to non-fault areas, and optimise the power flow of distribution networks. Based on the critical role of distributed generation (DG) power supply in fault recovery, this study proposes a method that leverages the synergistic capabilities of SOPs and DG to recover faults in distribution networks. By employing a binary particle swarm optimisation algorithm, this method effectively improves load recovery, reduces the number of switching operations, and minimises network loss. The proposed method was simulated and verified using the IEEE 33-node and 99-node systems.

The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.