With large-scale penetration of renewable energy sources (RES) into the power grid, maintaining its stability and security of it has become a formidable challenge while the conventional frequency regulation methods are inadequate to meet the power balance demand. Energy storage systems have emerged as an ideal solution to mitigate frequent frequency fluctuations caused by the substantial integration of RES. Flywheel energy storage systems (FESS) are considered environmentally friendly short-term energy storage solutions due to their capacity for rapid and efficient energy storage and release, high power density, and long-term lifespan. These attributes make FESS suitable for integration into power systems in a wide range of applications. A comprehensive review of FESS on the generation side of the power systems, coal-fired thermal power units, wind turbine power plants, photovoltaic panels, and integrated energy systems have been presented. Furthermore, flywheel energy storage system array and hybrid energy storage systems are explored, encompassing control strategies, optimal configuration, and electric trading market in practice. These researches guide the developments of FESS applications in power systems and provide valuable insights for practical measurements ,shaping the future prospects of this technology. 

Applications of flywheel energy storage system on load frequency regulation combined with various power generations: A review

The resilience management of energy and power systems is of utmost importance in mitigating the impact of extreme events, which have resulted in devastating disasters and substantial economic losses. We present a novel stochastic distributionally robust optimization approach for the resilience-oriented planning of integrated electricity and heat systems (IEHSs). Firstly, A resilience-oriented planning model is developed for the IEHS, which incorporates the hardening of both electricity and heat networks, while also considering the deployment of both electric and thermal energy storages to enhance the resilience of the IEHS as a whole. Then, the stage-by-stage uncertainties associated with extreme weather events faced by the IEHS are accounted for by a stochastic distributionally robust optimization approach, where the uncertainty in the intensity of contingent extreme events is addressed via a stochastic optimization approach, while the uncertainty in the occurrence of outages resulting from a specific extreme event is addressed by a distributionally robust optimization approach. Finally, the stochastic distributionally robust optimization model is transformed into an equivalent three-level model, which is solved using a customized column-and-constraint generation algorithm. The effectiveness and superiority of the proposed approach according to the enhanced resilience and reduced costs are demonstrated by numerical simulations. 

Resilience-oriented planning of integrated electricity and heat systems: A stochastic distributionally robust optimization approach

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs (μ-PMUs), and smart meters. However, large amount of data collected by these devices bring several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications. 

Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

Microgrid is a typical low-inertia system with uncertainty due to the high penetration of power electronics and renewable energy. Therefore, it is necessary to consider the issue of frequency security when planning microgrids. In this paper, we propose a frequency-constrained optimal planning approach involving both long- and short-term uncertainties to optimally design the critical equipment size for a microgrid while ensuring frequency security in case of a power disturbance. This approach explicitly considers the distinct characteristics of primary frequency responses (e.g., different delivery times) and formulates the frequency constraints as second-order cone constraints. The long-term uncertainties about load demand and unit investment cost of developing technology are addressed by a set of credible scenarios. In each long-term scenario, the short-term uncertainty in the operational stage associated with wind power is described by the Wasserstein-metric ambiguity set. To efficiently solve the proposed model, we first reformulate the distributionally robust joint chance constraint and bilinear terms to enable the proposed model as a mixed-integer second-order cone programming (MISCOP) and then a logic-based Benders decomposition is introduced to solve the MISOCP. Case studies demonstrate the effectiveness and scalability of the proposed method.

Stochastic-Distributionally Robust Frequency-Constrained Optimal Planning for an Isolated Microgrid

Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users’ optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method.

Demand-side Joint Electricity and Carbon Trading Mechanism

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the ``high emission with high price'' principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission.

pdf/encoding-carbon-emission-flow-in-energy-management-a-compact-2s86ogqv.pdf

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

In recent years, virtual power plants (VPPs) have been undergoing a rapid development to aggregate mushrooming distributed energy resources. In this paper, a distributed robust algorithm for VPPs’ peer-to-peer (P2P) energy trading is proposed which can improve the robustness against communication failures such as packet losses and computing node failures in the cyber layer. Firstly, a P2P energy trading framework suitable for VPPs located in the unbalanced distribution network managed by the distribution system operator (DSO) is proposed, which is a bi-level problem with the DSO standing at the higher level to solve the power flow model and VPP operators (VPPOs) following at the lower level to decide the bilateral trading quantity and prices. Herein, an enhanced linearized DistFlow model is developed to address the unbalanced distribution network. Then, a distributed algorithm wherein the DSO and VPPOs engage in an iterative negotiation process based on relaxed alternating direction method of multipliers (R-ADMM) is derived, which renders the VPPOs’ parallel computation and preserves privacy. Furthermore, a robust version of the algorithm against communication failures (DRAACF) is developed. The economy, robustness, computational efficiency and scalability of the proposed approach are validated by simulation analyses based on the modified IEEE 13-bus and IEEE 123-bus distribution networks.

Virtual Power Plants Peer-to-Peer Energy Trading in Unbalanced Distribution Networks: A Distributed Robust Approach Against Communication Failures

Determining the pricing and deployment strategy for virtual power plants of peer-to-peer prosumers: A game-theoretic approach

The role of electricity market design for energy storage in cost-efficient decarbonization

The line shunts are usually ignored by various linear power flow (PF) models in power distribution system analysis, planning and optimization. However, “charging effects” from line shunts of underground/submarine power cables would cause non-negligible model errors for these commonly used PF models. In this paper, we propose a modified Linear Distflow model (LinDist) with line shunts, i.e., LinDistS. We also further propose its extensions considering the ZIP load, weakly-meshed topology and unbalanced three-phase systems. Moreover, the linearization error of voltage component is theoretically analyzed. Case studies show that compared with other models, the proposed LinDistS achieves the descent calculation accuracy and efficiency. We also show the application scope of LinDistS by a Volt/VAr control (VVC) framework with distributed generators, mainly photovoltaic (PV), and the non-negligible “charging effect”. Simulation exhibits that with LinDistS, the VVC can optimally dispatch the shunt capacitors and also optimize the real-time reactive power output of PV. Moreover, with LinDistS, the VVC shows better solutions’ consistency and higher computing efficiency compared to traditional VVC methods. 

A linear Distflow model considering line shunts for fast calculation and voltage control of power distribution systems

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