Carbon-Oriented Electricity Balancing Market for Dispatchable Generators and Flexible Loads

The high renewables penetration results in increased imbalance volumes and balancing actions due to system stability requirements. The balancing market (BM) primarily turns down renewable generation and turns up traditional carbon-intensive generation in response to real-time energy imbalance. Existing dual-stage market mechanisms conflict with the carbon reduction trajectory by implementing balancing actions regardless of their carbon footprint. This paper proposes a novel carbon-oriented BM model to coordinate the environmental targets in the dayahead (DA) and real-time BMs. The emissions of dispatchable generators and flexible loads are distinguished by their operation modes and flexibility types, respectively. Carbon signals are incorporated into their bid&amp;#x002F;offer prices through the proposed carbon emission flow (CEF) model. By integrating these carbon incentives, the dual-stage market model is formulated to minimize economic and environmental costs. Simulation results demonstrate that, overall, although the proposed BM mechanism results in an increased cost of balancing services (159.10 m&amp;#x00A3;), there is a concurrent larger drop in carbon costs (294.14 m&amp;#x00A3;), resulting in a reduction in total cost. It enables system operators to incentivize decarbonized energy resources in DA scheduling and real-time balancing actions.</p

Carbon-Oriented Electricity Balancing Market for Dispatchable Generators and Flexible Loads

The high renewables penetration results in increased imbalance volumes and balancing actions due to system stability requirements. The balancing market (BM) primarily turns down renewable generation and turns up traditional carbon-intensive generation in response to real-time energy imbalance. Existing dual-stage market mechanisms conflict with the carbon reduction trajectory by implementing balancing actions regardless of their carbon footprint. This paper proposes a novel carbon-oriented BM model to coordinate the environmental targets in the dayahead (DA) and real-time BMs. The emissions of dispatchable generators and flexible loads are distinguished by their operation modes and flexibility types, respectively. Carbon signals are incorporated into their bid&amp;#x002F;offer prices through the proposed carbon emission flow (CEF) model. By integrating these carbon incentives, the dual-stage market model is formulated to minimize economic and environmental costs. Simulation results demonstrate that, overall, although the proposed BM mechanism results in an increased cost of balancing services (159.10 m&amp;#x00A3;), there is a concurrent larger drop in carbon costs (294.14 m&amp;#x00A3;), resulting in a reduction in total cost. It enables system operators to incentivize decarbonized energy resources in DA scheduling and real-time balancing actions.</p

Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.Comment: Submitted to IEEE Transactions on Sustainable Energ

Blockchain for secure decentralized energy management of multi-energy system using state machine replication

Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method

Economic-effective multi-energy management with voltage regulation networked with energy hubs

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wassersteain metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO problem enables to provide a voltage-regulated economic operation scheme with gas quality ensured that contributes high-quality but low-cost multi-energy supply to customers

Challenges and pathways of low-carbon oriented energy transition and power system planning strategy: a review

This paper provides an overview of the challenges and pathways involved in achieving a low-carbon-oriented energy transition roadmap and power system planning strategy. The transition towards low-carbon energy sources is crucial in mitigating the global climate change crisis. However, this transition presents several technical, economic, and political challenges. The paper emphasizes the importance of an integrated approach to power system planning that considers the entire energy system (including both physical and information systems and market mechanisms) and not just individual technologies. To achieve this goal, the paper discusses various pathways toward low-carbon energy transition, including the integration of renewable energy sources into current energy systems, energy efficiency measures, and market-based and regulatory strategies encompassing the implementation of regulations, standards, and policies. Furthermore, the paper underscores the need for a comprehensive and coordinated approach to energy planning, taking into account the socio-economic and political dimensions of the transition process. In addition, the paper reviews the methodologies used in modeling low-carbon-oriented power system planning, including both model-based methods and advanced machine learning-assisted solutions. Overall, the paper concludes that achieving a low-carbon-oriented energy transition roadmap and power system planning strategy requires a multi-dimensional approach that considers technical, economic, political, and social factors

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method