Power system decarbonization enables society-wide efforts to achieve carbon neutrality. This paper outlines the vision, pathways, and policy implications of constructing a new power system that is compatible with carbon neutrality goals. It analyzes eight key technological components of the new power system: renewable energy integration, new electricity transmission, flexible distribution networks, smart consumption and supply-demand interaction, energy storage regulation, grid digitalization, operation optimization, and carbon accounting. Their technological progress, demonstration projects, challenges, and trends are reviewed, and a technological roadmap is presented. The paper offers a point of reference for policy-making regarding power system transformation toward carbon neutrality.

With the increasing penetration of variable renewable energy, flexible resources are highly needed to hedge the growing uncertainty, and variability in the power system. Demand response has served as a cost-effective type of flexible resource in recent years. In order to balance the uncertainty of the system, it is crucial to assess how much flexibility demand response programs can provide. Thus, forecasting demand response potential is important for the operation of the bulk system. This paper proposes a modeling approach that can characterize the multi-timescale flexibility of demand response so that not only the power potential but also temporal-coupling characteristics can be considered. Furthermore, a day-ahead demand response potential forecasting method is proposed using deep convolutional generative adversarial networks. The proposed forecasting method is tested using data from 170 users in Pecan Street Dataport. The results show that the proposed method can forecast the multi-timescale flexibility of demand response with high accuracy.

The high renewable penetrated power system has severe frequency regulation problems. Distributed resources can provide frequency regulation services but are limited by communication time delay. This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service. Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation. We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance. Besides, we study communication resources allocation solution in high renewable energy penetrated power systems. We provide a case study based on the HRP-38 system. Results show communication time delay decreases distributed resources’ ability to provide frequency regulation service. On the other hand, allocating more communication resources to distributed resources’ communication services improves their frequency regulation performance. For power systems with renewable energy penetration above 70%, required communications resources are about five times as many as 30% renewable energy penetrated power systems to keep frequency performance the same.

With the prevalence of Electric Vehicles (EVs), a large number of on-board lithium batteries will be retired from EVs in the future. These Second-life Battery (SLBs) usually still preserve 70%–80% of their original capacities and have potential to be reutilized in the power system. However, at present, users in power systems such as renewable energy producers have little enthusiasm on SLBs, as the lifetime of SLB is usually shorter than a fresh battery and consistency of the SLBs’ performance is not as good as the fresh battery. To address above issues, this paper proposes a SLB utilization model based on the concept of Cloud Energy Storage (CES), namely SLB-based CES. In the SLB-based CES, the SLB is shared by multiple renewable energy producers. In order to comprehensively analyze the profit and investment risk of each participant in the CES system, the optimal scheduling model of the CES system is proposed. Multiple business models for the SLB supplier are designed. Economic analysis is carried out based on actual power system profiles from Western Inner Mongolia, China. Simulation results verify that the proposed SLB-based CES model can reduce energy storage utilization cost and mitigate the investment risk which is originally undertaken by energy storage users.

Monthly electricity consumption forecasting (ECF) plays an important role in power system operation and electricity market trading. Widespread popularity of smart meters enables collection of fine-grained load data, which provides an opportunity for improvement of monthly ECF accuracy. In this letter, a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed, which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy. The framework is formulated as a nested bi-layer optimization problem. A grid search method combined with a greedy clustering method is proposed to solve the optimization problem. Superiority of the proposed method has been verified on a real smart meter dataset.

The high penetration of variable renewable energy raises a flexibility challenge in the power system. This raises the necessity of considering the adequacy of flexibility in power system planning. However, the flexibility of the power system covers a wide range of timescales, from seconds to months. This poses difficulties in planning of multi-timescale flexible resources. This paper proposes a new perspective on the modeling and planning of multi-timescale flexible resources in power systems with high penetration of variable renewable energy. The operational boundaries of flexible resources are transformed into a characteristic domain, where flexibility at different timescales can be added and the balance of flexible supply and demand can be expressed as algebraic equations. Such modeling facilitates rigorous multi-timescale flexibility balance metrics. Furthermore, a planning method for multi-timescale flexibility is proposed based on the model in the characteristic domain. The proposed planning method is tested using data from China’s Xinjiang provincial power grid. Results show the proposed method can characterize multi-timescale flexibility with high accuracy, thus making it possible to fully account for flexibility at different timescales.

Integrating variable renewable energy is one of the most effective ways to achieve a low-carbon energy system. The high penetration of variable renewable energy, such as wind power and photovoltaic, increases the challenge of balancing the power system. Energy storage technology is regarded as one of the key technologies for balancing the intermittency of variable renewable energy to achieve high penetration. This study reviews the energy storage technology that can accommodate the high penetration of variable renewable energy. The basic energy storage technologies that can accommodate time-scale variation are reviewed first. The role of energy storage in the generation, transmission, distribution, and consumption for the high variable renewable energy penetration system is then analyzed. The supporting energy storage policies in the United States, the United Kingdom and China are summarized. Specific suggestions are proposed from the perspectives of technology, business and policy. This paper provides guidelines for planning energy storage to enable a high variable renewable energy penetration power system.