Sustainable Energy Grids & Networks最新文献

Information theory can be a useful tool for quantifying the perturbations in the associated state variables at the time of disturbance occurrence. The study introduces a framework for the spectral decomposition of multivariate information measures to detect initiation of low frequency oscillations (LFOs), caused due to physical events in the power grid. A frequency-specific quantification of the information is shared between a target variable and two source variables from their time series data. Initially, the approach is applied on different synthetic test signals having different oscillatory frequency modes and decay time constant. Then, approach is extended on PMUs signals. The combination of cross-spectral and information-theoretic approaches is applied for the multi-variable analysis of PMUs signals from the same bus. The interdependence among the frequency, voltage angle and voltage magnitude, corresponding to specific oscillations, manifested due to cause-effect relationships obtained in terms of statistics is estimated. The dynamics in terms of unique (interaction), redundant and synergetic information is determined with the contribution from two of these three signals as source variables to target variable (frequency/voltage angle). This provides a direct coupling to identify driver-response relationships between source variables and target variable to indicate the onset of LFOs, following physical events in power network. The extension of approach among the variables from different buses aids to identify the responsible area of event occurrence.

Coordinating residential building groups requires a hierarchical structure in which aggregate objectives and coupled constraints are incorporated into decision-making processes at different layers of the electric distribution system. Failure to handle these matters can raise issues, such as rebound peaks and contingencies. This paper proposes a Hierarchical Transactive Coordination Mechanism (HTCM) capable of dealing with residential consumers’ objectives/constraints and local and grid coordinators’ shared objectives/coupled constraints under a bottom-up strategy. Particularly, the proposed multi-level framework distributes local and grid coordinators’ shared objectives among consumers to flatten the aggregate consumption profile and minimize the aggregate energy cost at each level. The suggested scheme is enhanced by developing two additional operations. A gain-sharing technique is designed to fairly divide the total gain acquired by the grid coordinator across the hierarchy from higher to lower levels, successively. Besides, a coupled constraint-sharing method is devised to link these levels and fulfill the coupled constraints by revising consumers’ decisions. The proposed approach is applied to a society of buildings comprising Home Energy Management System (HEMS) groups with demand response-enabled electric Baseboard Heaters (BHs), and its effectiveness is investigated through different case studies. The results demonstrate that the recommended HTCM is able to improve the society’s aggregate power profile load factor by 89%, from 0.45 up to 0.85, and decreases its overall electricity cost by 6.2%.

This paper proposes a method to spatially model and compare charging needs on the European scale, considering local disparities in population density, distance to city centres, car ownership and mobility habits. Mobility habits are modelled across Europe in terms of distance and time frame to elaborate scenarios of charging behaviour. The first step of the method is to calculate the density of electric vehicles with a resolution of 1 km${}^2$, according to the progressive electrification of the fleet each year between 2020 and 2050. The second step is to quantify the mobility of commuters using their driving distance to work areas and mobility statistics. The model is then applied in a case study in Switzerland to plan the public charging infrastructure required to satisfy the charging needs of the local population. Despite lower motorization rates and driving distances, the results show a stronger need for charging in cities. With 50% of commuters charging at work and 20% at home during the workday, the demand in the evening can be reduced by 50% in the suburban areas compared to the baseline scenario in which all commuters are charging at home in the evening. This model can be used to quantify the energy needs of commuters, plan the deployment of the charging infrastructure, or simulate the effect of policies.

Decarbonization of the transportation sector relies on the widespread adoption of Electric Vehicles (EVs) and appropriate charging strategies. However, uncoordinated EV charging can adversely affect the power grid, and effective scheduling schemes are necessary to mitigate adverse effects. This study aims to develop bi-objective optimization models for EV charging scheduling at a workplace charging station, addressing the EV users’ preferences in terms of economic and Quality-of-Service (QoS) dimensions, by minimizing the charging cost considering the participation in Vehicle-to-Grid (V2G) schemes and minimizing the deviation from the desired State-of-Charge (SoC). To address this deviation, two perspectives are considered: minimizing the sum of deviations, embodying a compensatory criterion, and minimizing the worst deviation, a fairness criterion based on a min-max approach. To obtain a representation of the non-dominated solution set corresponding to the scheduling plan for each EV, the Epsilon-constraint method is used. Furthermore, machine learning techniques are employed to predict the charging behavior of EV users, including the desired SoC and charging budget. A sensitivity analysis is also conducted to explore the influence of energy selling prices in V2G mode to accommodate EV users’ preferences. The findings indicate that as the difference between the energy buying and selling prices increases, it becomes more challenging to satisfy the desired SoC based on the defined charging budget. Additionally, the model that aims to minimize the charging cost and the worst-case deviation to the desired SoC is more sensitive to changes in energy selling prices, highlighting the impact of price variations in scheduling plans.

The exposure to High-Impact Low-Probability (HILP) events can have significant impact on the performance of transmission networks. Under such conditions, the power system components must be resilient and robust to meet the uninterrupted load demand. This paper proposes a quantitative framework for temporal assessment of operational resilience of transmission network and suggests suitable adaptation measures when the system is subjected to high impact cyclonic windstorm lasting for a long duration. The fragility curves of transmission lines are correlated with wind profiles during cyclone and failure probability each transmission line is determined using the Monte Carlo Simulation (MCS) The operational resilience of transmission networks is quantified by computing Total Transfer Capability (TTC), Available Transfer Capability (ATC), Existing Transmission Uses (ETU), Total Reliability Margin (TRM) and unserved load. To improve the operational resilience, adaptation measures with modifications in the robustness of the structural strength is proposed and investigated on standard IEEE 57 bus system and IEEE 118 bus system. Sensitivity analysis is performed to understand how changes in the percentage increase of robustness affect the overall system performance. The findings give valuable insights for evaluating the operational resilience of transmission line infrastructure during such extreme weather events.

In this paper we present an efficient model predictive control algorithm for distributed coordination of an electrical power system comprising many spatially distributed controllable units. The coordination problem is formulated using parametric solutions of local optimization problems corresponding to individual subsystems leading to favorable problem structure which can be split across all subsystems and nodes in the network. The structure of the obtained coordination problem is exploited to develop a very efficient solution algorithm based on an ADMM technique. The key features of the overall approach are: (i) private data of individual subsystems are protected, (ii) simple and efficient on-line computations, (iii) parallelization of computation across all subsystems and all nodes in the network. Efficiency of the proposed control strategy is demonstrated on a number of numerical case studies of varying size.

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independent of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with weather and electricity price forecasts. A Stochastic Mixed-Integer Linear Program (SMILP) is proposed to maximize Sample Average Approximation (SAA) of expected profit within a specified scenario space. The SMILP exhibits robust performance, yet its computational time poses a challenge. Three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate Dispatch Plans (DPs). Subsequently, the DP with the best average performance on all profiles is selected. The new methods are applied to a hypothetical 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6–9 % higher profit than the closest heuristic when the DPs are applied to novel weather conditions. With a large historical weather dataset, the performance of the SMILP and closet heuristic becomes nearly identical since the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, the heuristic emerges a practical alternative, providing similar average profit in a reasonable time. Taken together, the results illustrate the importance of considering uncertainty in DP optimization and indicate that straightforward heuristics on a large database are a practical method for addressing uncertainty.

This study provides insights into possible future gas network states in the initial German hydrogen network by 2025 and 2030, as per the German transmission system operators Network Development Plan Gas 2020. Not only is the overall transport feasibility assessed, but also possible operating conditions in terms of pressures, flows and velocities. To that end, two data sets for the network topology by 2025 and 2030 were created. A heuristic, semi-random nomination generation is employed to generate 100 consistent steady-state source–sink nominations for both years, based on collected production/consumption bounds. The authors employ a so-called nomination-validation model (MILP-formulation) for the solution of the resulting transport problem(s). For the evaluation of pipeline flow velocities, the authors combine those solutions with a hypothesis on limiting flow speeds suggested in a German technical journal. The analysis exhibits feasibility among all generated nominations with respect to flows and admissible velocities.

Multi-energy system with distributed energy resources has become the inevitable trend in recent years due to their potential for creating the efficient and sustainable energy infrastructure, with a strong ability on carbon emission reduction. To accommodate the uncertainties of renewable energy generation and energy demand, model-free deep reinforcement learning methods are emerging for energy management in multi-energy system. However, traditional reinforcement learning methods still have operation safety issue of violating the physical constraints of multi-energy system. To address the challenges, a low-carbon scheduling strategy based on safe soft actor-critic algorithm is proposed in this paper. Firstly, an electricity-thermal-carbon joint scheduling framework is constructed, where carbon trading mechanism is incorporated to further motivate carbon emission reductions. Secondly, the energy cost and carbon trading cost are simultaneously integrated in the objective function, and the dynamic optimization problem of multi-energy system is modeled as a constrained Markov decision process by taking into account the diverse uncertainties. Then, a novel safe soft actor-critic method is proposed to achieve the benefits of economic and carbon emissions, where the security networks and Lagrangian relaxation are introduced to deal with operation constraints. The case study validates that the proposed scheduling strategy can reduce the energy cost and carbon trading cost by up to 26.24% and 33.73% within constraints, compared with existing methods.

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by $\sim 11.06\text{\%}$ while the energy use cost of consumers reduces by $\sim 4.80\text{\%}$ using the proposed approach for the system under study.