Long-Term Power System Modeling and Optimization with Energy Storage Integration

Introduction: In the context of the evolving energy landscape, the efficient integration of energy storage systems (ESS) has become essential for optimizing power system operation and accommodating renewable energy sources.Methods: This study introduces LoadNet, an innovative approach that combines the fusion of Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models, along with a self-attention mechanism, to address the challenges associated with ESS integration in power system operation. LoadNet aims to enhance the management and utilization of ESS by effectively capturing the complex temporal dependencies present in time-series data. The fusion architecture of TCN-GRU in LoadNet enables the modeling of both short-term and long-term dependencies, allowing for accurate representation of dynamic power system behaviors. Additionally, the incorporation of a self-attention mechanism enables LoadNet to focus on relevant information, facilitating informed decision-making for optimal ESS operation. To assess the efficacy of LoadNet, comprehensive experiments were conducted using real-world power system datasets.Results and Discussion: The results demonstrate that LoadNet significantly improves the efficiency and reliability of power system operation with ESS. By effectively managing the integration of ESS, LoadNet enhances grid stability and reliability, while promoting the seamless integration of renewable energy sources. This contributes to the development of a more sustainable and resilient power system. The proposed LoadNet model represents a significant advancement in power system management. Its ability to optimize power system operation by integrating ESS using the TCN-GRU fusion and self-attention mechanism holds great promise for future power system planning and operation. Ultimately, LoadNet can pave the way for a more sustainable and efficient power grid, supporting the transition to a clean and renewable energy future.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

The need to integrate energy storage systems (ESS) with warship power systems to meet future dynamic loads such as high power electric weapons is apparent. This opens up challenges with design integration of ESS with power systems and operational aspects such as steady-state, transient and faulted performance. This paper describes the integration of ESS with a candidate power system as a case study as part of an ongoing timedomain simulation investigation at University College London. The paper describes the models and power management structure of the simulation testbed, that comprises battery based ESS and diesel generators in a hybrid electric power and propulsion system. The results of two scenarios are presented, the first verifies power sharing between a diesel generator and ESS during load levelling under single generator operation, the second illustrates the ability of the ESS to provide ride through power during a generator fault on the main distribution bus. The conclusions suggest that under voltage in the candidate system outside of acceptable limits occurs during fault ride through when in single generator operation.

Integrating intermittent renewable energy sources into the power grid poses significant challenges to grid stability and reliability. This study examines the integration of Energy Storage Systems (ESS) into power grids to enhance stability and performance. A simulation framework was developed to analyze the technical and economic viability of Battery Energy Storage Systems (BESS) and Pumped Hydro Storage (PHS) systems. The results demonstrate the effectiveness of ESS in alleviating voltage fluctuations, frequency deviations, and grid disturbances. A diversified energy storage portfolio with optimized siting and innovative market mechanisms maximizes the benefits of ESS integration. The study reveals that BESS excel in rapid response times and scalability, while PHS systems offer superior economic benefits. This study pioneers a comprehensive simulation framework integrating technical, economic, and regulatory aspects to optimize Energy Storage Systems (ESS) integration, providing novel insights into maximizing grid stability and performance amidst escalating renewable energy integration. The findings provide valuable insights for policymakers, industry stakeholders, and researchers seeking to optimize energy storage strategies for resilient, sustainable, and efficient power systems.

Integrating intermittent renewable energy sources into the power grid poses significant challenges to grid stability and reliability. This study examines the integration of Energy Storage Systems (ESS) into power grids to enhance stability and performance. A simulation framework was developed to analyze the technical and economic viability of Battery Energy Storage Systems (BESS) and Pumped Hydro Storage (PHS) systems. The results demonstrate the effectiveness of ESS in alleviating voltage fluctuations, frequency deviations, and grid disturbances. A diversified energy storage portfolio with optimized siting and innovative market mechanisms maximizes the benefits of ESS integration. The study reveals that BESS excel in rapid response times and scalability, while PHS systems offer superior economic benefits. This study pioneers a comprehensive simulation framework integrating technical, economic, and regulatory aspects to optimize Energy Storage Systems (ESS) integration, providing novel insights into maximizing grid stability and performance amidst escalating renewable energy integration. The findings provide valuable insights for policymakers, industry stakeholders, and researchers seeking to optimize energy storage strategies for resilient, sustainable, and efficient power systems.

A comprehensive review on techno-economic assessment of hybrid energy storage systems integrated with renewable energy

Special issue on “Optimal design and operation of energy systems”

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...]

The distribution networks are experiencing important changes driven by the massive integration of renewable energy conversion systems. However, the lack of direct controllability of the Distributed Generations (DGs) supplying Active Distribution Networks (ADNs) represents a major obstacle to the increase of the penetration of renewable energy resources characterized by a non-negligible volatility. The successful development of ADNs depends on the combination of i) specific control tools and ii) availability of new technologies and controllable resources. Within this context, this thesis focuses on developing practical and scalable methodologies for the ADN planning and operation with particular reference to the integration of Energy Storage Systems (ESSs) owned, and directly controlled, by the Distribution Network Operators (DNOs). In this respect, an exact convex formulation of Optimal Power Flow (OPF), called AR-OPF, is first proposed for the case of radial power networks. The proposed formulation takes into account the correct model of the lines and the security constraints related to the nodal voltage magnitudes, as well as, the lines ampacity limits. Sufficient conditions are provided to guarantee that the solution of the AR-OPF is feasible and optimal (i.e., the relaxation used is exact). Moreover, by analyzing the exactness conditions, it is revealed that they are mild and hold for real distribution networks. The AR-OPF is further augmented by suitably incorporating radiality constraints in order to develop an optimization model for optimal reconfiguration of ADNs. Then, a two-stage optimization problem for day-ahead resource scheduling in ADNs, accounting for the uncertainties of nodal injections, is proposed. The Adaptive Robust Optimization (ARO) and stochastic optimization techniques are successfully adapted to solve this optimization problem. The solutions of ARO and stochastic optimization reveal that the ARO provides a feasible solution for any realization of the uncertain parameters even if its solution is optimal only for the worst case realization. On the other hand, the stochastic optimization provides a solution taking into account the probability of the considered scenarios. Finally, the problem of optimal resource planning in ADNs is investigated with particular reference to the ESSs. In this respect, the AR-OPF and the proposed ADN reconfiguration model, are employed to develop optimization models for the optimal siting and sizing of ESSs in ADNs. The objective function aims at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedures accounts for (i) network voltage deviations, (ii) feeders/lines congestions, (iii) network losses, (iv) cost of supplying loads (from external grid or local producers) together with the cost of ESS investment/maintenance, (v) load curtailment and (vi) stochasticity of loads and renewables production. The use of decomposition methods for solving the targeted optimization problems with discrete variables and probable large size is investigated. More specifically, Benders decomposition and Alternative Direction Method of Multipliers (ADMM) techniques are successfully applied to the targeted problems. Using real and standard networks, it is shown that the ESSs could possibly prevent load and generation curtailment, reduce the voltage deviations and lines congestions, and do the peak shaving.

As the penetration of renewable energy generation increases, the importance of energy storage systems becomes evident since these systems can contribute for the preservation of the power system stability. Wind turbine owners can also benefit from having energy storage systems as they can increase their revenues. The fast growth of wind turbine power ratings will eventually lead to the requirement of higher voltage levels as well. Proper power electronic converters will be required to drive these systems. Converters with a modular multilevel structure are considered the state-of-the-art solution for high-power applications. These topologies allow for a flexible integration of energy storage systems in both centralized and decentralized ways. This article presents a new converter solution with a modular multilevel structure and decentralized energy storage integration suitable to drive high-power medium-voltage wind turbines. This converter presents important structural and control characteristics that allows for a straightforward integration of an energy storage system in such a way that the wind turbine driven by it can operate with high flexibility and in a dispatchable fashion, benefiting both the power system operator and the wind-power-plant owner.

Careful design and planning is essential for successful integration of energy storage system (ESS) in a shipboard dc hybrid power system. An optimization model for resiliency enhancement and total cost reduction is proposed in this paper. The resilience of a shipboard power system with and without ESS are evaluated by means of resiliency index values under fault conditions. Furthermore, the influence of ESS on total cost is analyzed quantitatively. A detailed design of ESS is carried out, and optimal ESS configurations with different device combinations for various requirements are determined. Both single-node static and dynamic planning strategies are evaluated and the principles of planning strategy selection are discussed. Several key results are presented from the optimization model, built on General Algebraic Modeling System (GAMS) platform. Simulation results show significant improvement from the proposed design and planning of ESS on shipboard dc hybrid power system.

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

This study introduces a novel and effective approach to address the multi‐area dynamic economic dispatch problem, with the primary objective of minimizing both operational costs and water consumption associated with the dispatch process. This research is motivated by the growing complexity of modern power systems, which require efficient management of both operational costs and resource consumption (e.g. water) to ensure sustainability and reliability. The proposed model simultaneously optimizes two critical objectives: the total operational cost, comprising thermal energy production, wind energy integration, power transfer between regions, pumped energy storage operations and water consumption and the overall water usage. To enhance the model's relevance to contemporary power systems, several key features are incorporated, including the integration of wind energy, the deployment of energy storage systems, the interconnection of geographically diverse regions within the power grid and the implementation of a demand response (DR) mechanism to mitigate peak loads and improve system efficiency. To tackle the complexity of balancing multiple objectives and constraints, a novel optimization method based on the combined whale optimization algorithm and grey wolf optimizer is developed. By integrating these techniques, the method effectively explores a broader solution space, offering a more accurate and efficient optimization process without the need for additional chaotic mechanisms or differential evolution. Solving the optimization problem on a 40‐unit test system without DR resulted in a 6% reduction in water consumption compared to the initial conditions. With the integration of DR, the hybrid method achieved further improvements, reducing the total cost by 9.56% and water consumption by 3.05% compared to the case without DR. These results demonstrate the effectiveness of the proposed approach and the added value of DR in improving both economic and environmental performance. This study contributes to the ongoing efforts in designing more efficient, sustainable and resilient power systems.

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids