Sustainable Energy Grids & Networks最新文献

Given the unbalanced distribution of power resources and demands in geography, cross-regional electricity transactions alleviate the conflict through the long-distance power supply. To ensure sustainable, efficient transactions, the market mechanism addressing the unavoidable transmission charges is essential for balancing the interests of all parties. This research designs a mechanism based on the Generalized Vickrey-Clarke-Groves (G-VCG) and threshold value setting considering generators' withholding behavior and power transmission charges. The theoretical analysis proves that this mechanism maximizes social welfare while satisfying individual rationality, incentive compatibility and weak budget balance. It can encourage all participants to report truthful information and motivate more power generation. Numerical studies of the PJM electricity market also demonstrate the effectiveness of this mechanism in the electricity market. The proposed mechanism contributes to new guidance and practical references for achieving fair and efficient transactions in the crossing-regional electricity market and improving the vigor of market participants.

This paper investigates the way in which the design of a TSO-DSO coordinated flexibility market can enable strategic behavior by flexibility service providers (FSPs). Multiple flexibility market models are considered for the procurement of flexibility services by transmission and distribution system operators, namely: a common (joint) market, a fragmented market, and a sequential multi-level market. Considering these market models, three non-cooperative games are introduced to investigate the strategic bidding and interaction between FSPs therein. Detailed conclusions are then drawn on the existence and uniqueness of Nash Equilibria (NEs) in the developed games, including derivations of closed-form expressions of the resulting NEs and corresponding price-of-anarchy, capturing the FSPs’ strategic bidding impact on the markets’ efficiency. The analysis considers – first in a duopoly setting, then with multiple players – three different use cases representing when: (1) a sufficient flexible capacity exists (sufficient flexibility offered from the FSPs and adequate interconnection/grid capacity between systems); (2) participants have a scarce flexibility capacity; and (3) a restrictive interface capacity exists between the systems. A case study considering an interconnected transmission–distribution system and multiple FSPs corroborates the analytical findings. The obtained results show that market participants have incentives to set bid prices greater than their marginal costs, thus decreasing the markets’ efficiency. This aspect is shown to be more pronounced when the available flexible capacity is limited, a restrictive line limit is present, or when the market is fragmented, thus supporting the need for additional network investments and the creation of joint flexibility market formats.

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

Hosting capacity (HC) and dynamic operating envelopes (DOEs), defined as dynamic, time-varying HC, are calculated using three-phase optimal power flow (OPF) formulations. Due to the computational complexity of such optimisation problems, HC and DOE are often calculated by introducing certain assumptions and approximations, including the linearised OPF formulation, which we implement in the Python-based tool ppOPF. Furthermore, we investigate how assumptions of the distributed energy resource (DER) connection phase impact the objective function value and computational time in calculating HC and DOE in distribution networks of different sizes. The results are not unambiguous and show that it is not possible to determine the optimal connection phase without introducing binary variables since, no matter the case study, the highest objective function values are calculated with mixed integer OPF formulations. The difference is especially visible in a real-world low-voltage network in which the difference between different scenarios is up to 14 MW in a single day. However, binary variables make the problem computationally complex and increase computational time to several hours in the DOE calculation, even when the optimality gap different from zero is set.

The urgent global concern regarding climate change has highlighted the necessity for transitioning to power generation with zero carbon emissions to promote a sustainable and environmentally conscious society. A crucial element in this transformation is reducing our dependence on the primary grid, which is predominantly powered by fossil fuels, natural gas, and coal. An innovative strategy for achieving this essential transition is through peer-to-peer energy trading (P2PET). However, the effectiveness of P2PET relies on successfully aligning the energy-related objectives of its participants. Identifying and effectively addressing these goals is a significant challenge. In response, this paper introduces a game-theoretic framework designed to encourage subscribers to engage in P2PET, both in islanded microgrids and interconnected grid configurations. Our methodology begins by introducing a model that captures the core energy-related objectives of both energy producers and consumers. This model is supported by a layered architectural framework tailored for peer-to-peer (P2P) marketplaces, enhancing the identification and classification of existing technologies in this domain. Following this, we delve into the formulation of an extended-form game rooted in non-cooperative game theory. We systematically evaluate the presence of strict Nash equilibria within this game-theoretic structure. To promote active engagement and trading in the peer-to-peer energy market (P2PEM), we introduce an innovative energy allocation policy. This policy is strategically devised to ensure the inclusion of every subscriber in the market, irrespective of fluctuations in supply and demand dynamics. Our proposed P2PET scheme is tested on a representative system, specifically a 14-bus IEEE network, incorporating 8 energy producers and 11 consumers as active participants in the market. By conducting an extensive series of tests, we accurately evaluate the design's performance. The results, compared to previous studies, show a significant reduction in consumer energy bills, ranging from 33 % to 7 %. This convincing result underscores the effectiveness and robustness of our proposed energy trading framework. In a world grappling with the imperative to transition to sustainable energy practices, our game-theoretic approach to incentivizing participants in P2PET emerges as a pivotal contribution. It demonstrates tangible benefits, promotes green energy production, and encourages responsible energy consumption.

The presence of uncertain parameters in power systems has led to many challenges for the designers and operators of these systems. One of these challenges is reactive power management in the presence of distributed renewable generation sources.

In this article, the management of reactive power in distribution networks in the electricity market and the presence of distributed renewable generation sources, including wind and solar power plants, is performed considering the uncertainties in the network load, power generation of distributed generation sources, and active and reactive power market prices. Furthermore, reactive power cost modeling of reactive power compensation equipment is carried out.

A hybrid stochastic/robust optimization method is employed to model the uncertainties in the problem. Finally, the efficiency of the method is confirmed by numerical examinations using the IEEE 33-bus distribution network and the GAMS optimization software. Simulation results indicate that in the risk-averse strategy, for a certain increase in cost, the radius of uncertainty in the active and reactive power market prices increases. Also, in this strategy, as β increases, the total cost of network operating increases by 81.72 %, while in a risk-seeking strategy, with the increase of β, the total operating cost of the network decreases by 77.78 %.

Energy poverty has been increasing since the early 2020s because of rising energy prices. This is attributed to geopolitical crises and the inclusion of the energy cost of ${\mathrm{CO}}_2$ pricing, which was historically an externality. Policymakers and citizens need new tools to address this issue, and energy communities are recognized as a valuable tool for mitigation. This study proposes two complementary approaches that relate to energy poverty and Renewable Energy Communities (RECs). The first aims to define and map energy poverty to support the policy in targeting measures and incentives. Using publicly available data, a new methodology is proposed for mapping energy poverty risk over a large territory with a fine granularity. The second approach taken sees REC managers at the center, who are tasked with sharing the economic benefits appropriately and equitably. A series of multi-criteria sharing mechanisms were developed and compared with the existing ones (e.g., based on Shapley value), including the energy poverty mitigation among them and the assessment of the impact of RECs on it. The results show that sharing methods can be one of the viable pathways for mitigating energy poverty through RECs without compromising the economy of non-vulnerable REC members.

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

Delays, jitter, and packet loss in communication networks can impact the performance of cooperative voltage control systems in distribution networks. In distribution systems with a high penetration of renewable energy sources that do not respond promptly, these issues can even lead to system destabilization when voltage surges occur. Considering the interdependence of delay, jitter, and packet loss, the current direct approach of accumulating information entropy may result in the deterioration of dynamic control performance. Based on Copula entropy theory, a new multivariate communication uncertainty metric model is proposed. Using the multivariate Epanechnikov kernel function model, a method has been developed to estimate the multivariate non-independent uncertainty of a communication system. Accurate state estimation is integrated into event-triggered sliding mode control (ETSMC) of the distribution network to facilitate coordinated voltage control and enhance resilience against communication uncertainty. Design criteria for the controller and observer parameters are provided based on Lyapunov stability theory. Simulation results confirm that the proposed ETSMC offers significant improvements in control performance and system resilience to external power disturbances and multivariate communication uncertainty events.

Security assessment is one of the most crucial functions of a power system operator. However, growing complexity and unpredictability make this an increasingly complex and computationally difficult task. In recent times, machine learning methods have gained attention for their ability to handle complex modeling applications. Convolutional neural networks (CNNs) in particular, are widely used in literature for their adaptability for classification problems. While CNNs generate promising results and some real-time advantages, they still require long training times and computational resources. This paper proposes a graph neural network (GNN) approach to the small-signal security assessment problem using data from Phasor Measurement Units (PMUs). Using a GNN, the process for small signal security assessment can be optimized, reducing the time needed from minutes, to less than a second, thus allowing for faster real-time application. Also, using graph properties, optimal PMU placement is determined and the proposed method is shown to perform efficiently under partial observability with limited PMU data. Case studies with simulated data from the IEEE 68-bus system and the NPCC 140-bus system are used to verify the effectiveness of the proposed method showing comparisons with the CNN.