Sustainable Energy Grids & Networks最新文献

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Considering the residential sector, greenhouse gas emissions can be effectively reduced by the widespread deployment of distributed renewable energy sources (DRESs) in low-voltage (LV) electrical networks (ENs). Through the electrification and the integration of different energy systems, the exploitation of the locally generated renewable energy can be further increased, acting also as an efficient countermeasure to the technical challenges in ENs posed by the intermittent nature of DRESs. This work deals with the modeling of multi-energy systems (MESs) consisting of unbalanced ENs and district heating networks (DHNs), formulated based on existing EN and DHN models. The developed model is enhanced by the incorporation of a thermal droop control scheme into the controllable sources of DHN, i.e., heat pumps (HPs). The validity of the proposed model is assessed via time-series simulations on a MES composed of a benchmark unbalanced LV EN and a real DHN. It is shown that the integrated droop control scheme can act as a means towards overvoltage mitigation, temperature control and reduced MES losses, improving the operational reliability and exploitation of the DRES potential. Therefore, the proposed model could be useful for system operators and decision makers for the efficient planning and operation of MESs.

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of ±10 % was achieved. For an average area of 1 km², the error was 44.5 %, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.

The time delay is inevitable in the communication process of actual microgrids (MGs), which may lead to controller failure and even affect stability. This paper proposes a distributed weighted average prediction (WAP) control for the secondary control of islanded MGs with time delay and analyzes its delay margins. Firstly, a secondary control strategy is designed to achieve the frequency and average voltage recovery and accurate active and reactive power-sharing. Secondly, a WAP strategy is proposed to improve the delay margin of the designed MG system. Finally, the stability and delay margin of the MG system is analyzed in the frequency domain and a rigorous formula is derived to calculate the delay margin. Compared with the system without WAP control, the delay margin of the system can be increased by 15.8%. The simulation results and the experimental results based on the StarSim Modeling Tech Real-time experimental platform verify the effectiveness and feasibility of the proposed method. The results demonstrate that the proposed control strategy can improve the delay margin of the system. The proposed analysis method can obtain the expression of specific system delay margins, which can guide the parameter design.

The Transmission System Operators (TSO) and Distribution System Operators (DSO) coordination literature deals with different coordination schemes or coordination methodologies. However, consumer actions or regular DSO operations continuously affect the system balance operation, and no major coordination is required as these actions individually have negligible impacts on the overall system. The literature has not previously analysed where the limit beyond which coordination is necessary. This question requires an analysis of the DSO operations where the need for coordination is foreseen and a case-by-case study of what type of impacts are created by the activation of the DSO flexibility resources on the responsibilities of the TSO. Such analysis helps to define thresholds and scenarios considering existing changes in distribution networks, which can be a reference for delimitating costly coordination procedures. This paper presents a revision of all the possible scenarios of the DSO operation needs and their impacts on TSO responsibilities considering the possible TSO/DSO borders at different voltage levels. Afterward, a methodology is proposed to analyse more deeply the impact of flexibility activation with an expected significant load increase. Representative case studies evaluate the possible impacts on TSO responsibilities of local flexibility activation. This paper concludes that the impact of local flexibility is expected to be significant when large power changes are managed in the short term, estimated in more than 50 MW if the DSO operates in 132 kV or more than 15 MW if the DSO operates up to 66 kV. At LV or MV level, minor coordination would be needed.

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

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.