Energy Conversion and Economics最新文献

To promote the profit of generation companies and reduce carbon emissions from the generation sector, a two-layer decision-making model based on the cooperative game is proposed. Based on the analysis of a renewable-fossil energy generation alliance to participate in electricity-carbon-green certificate markets, a robust optimization model considering the price uncertainty in the electricity spot market is first established based on the information gap decision-making theory as the upper part of a two-layer optimization model. Subsequently, the clearing models of the electricity spot, carbon emission trading, and green certificate markets are established. This two-layer optimization model is transformed into a single-layer model based on the well-established KKT conditions. A profit allocation model for the members of the generation alliance is then presented based on the Shapley value and an improved nucleolus kernel method. Finally, the effectiveness of the proposed model is demonstrated by the IEEE 14-bus power system.

Peer-to-peer (P2P) energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real-time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two-stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game (GNG), considering that prosumers are self-interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy-preserving algorithm to guide prosumers toward the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33-power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

A power system can be regarded as a cyber-physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non-intrusive load monitoring (NILM), an emerging smart-grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection-based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber-attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection-based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real-time assessment of power- loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

Robust optimization is an essential tool for addressing the uncertainties in power systems. Most existing algorithms, such as Benders decomposition and column-and-constraint generation (C&CG), focus on robust optimization with decision-independent uncertainty (DIU). However, increasingly common decision-dependent uncertainties (DDUs) in power systems are frequently overlooked. When DDUs are considered, traditional algorithms for robust optimization with DIUs become inapplicable. This is because the previously selected worst-case scenarios may fall outside the uncertainty set when the first-stage decision changes, causing traditional algorithms to fail to converge. This study provides a general solution algorithm for robust optimization with DDU, which is called dual C&CG. Its convergence and optimality are proven theoretically. To demonstrate the effectiveness of the dual C&CG algorithm, we used the do-not-exceed limit (DNEL) problem as an example. The results show that the proposed algorithm can not only solve the simple DNEL model studied in the literature but also provide a more practical DNEL model considering the correlations among renewable generators.

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 (PEM) 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. Afterwards, 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.