• Energy Research

The efficiency of asset utilization is one of the core contents of Smart Grid. This paper proposes the line Power Flow Duration Curve (PFDC) as a tool in the process of transmission cost allocation. The impact of the power flow duration time on the transmission asset utilization can be taken into account effectively. An implementation mechanism is designed. The case studies based on the IEEE 30-bus and 118-bus system demonstrate that the transmission cost can be allocated much more reasonably by the proposed method.

Driven by the accelerating deployment of distributed energy resources (DERs), advanced information and communications technologies (ICTs), and demand-side response (DR), modern power systems have been transforming from a one-way energy supply chain to a two-way energy system. The continuous integration of DERs into a low-voltage distribution network brings profound challenges to its secure and economic operation, including technical issues with protection placement and coordination, voltage and power violation, power quality, and energy losses. To overcome these challenges, tremendous endeavours have been devoted to enhancing the accommodation capability for renewables in distribution networks. In addition, DERs, including both generation and storage resources, are gaining increasing attention to support the operation of bulk power grids by providing ancillary services, particularly in frequency regulation. To help researchers and engineers have a better overview of the state-of-the-art on the accommodation of renewables in power distribution networks, this special issue solicits original and novel research on enhancing hosting capability for renewable energy generation in active distribution networks. After undergoing a thorough peer review process, eleven papers were finally accepted on topic areas of operation and planning of distribution networks, modelling of DERs, virtual power plants, electricity market design, and demand-side management. A brief discussion of the authors' contributions is presented as follows.

Abstract In China, the electricity sector consumes approximately 50% of the coal and emits 40% of the CO2 from fossil fuel combustion. The unbalanced spatial distribution between energy resources and demands and the remarkable differences in power-generation capabilities among regions are important factors that impede decarbonization of China's electricity sector. Utilization of the abundant low-carbon energy resources in the central and western regions is restricted by limited local demand. Energy demand in these regions accounts for approximately 26% of the entire nation's demand. By comparison, the regions have more than 45% of the energy resources. However, long-distance energy delivery incurs considerable losses. At present, approximately 80% of inter-regional energy delivery uses primary coal transport and 20% travels by secondary electricity transmission. The Chinese government is planning to build an ambitious inter-regional transmission grid for energy delivery. We demonstrate that this plan would significantly change the current delivery patterns and improve delivery efficiency. Approximately 40% of inter-regional energy delivery would travel by secondary electricity transmission and a 25% improvement in the delivery efficiency of the entire system is expected. Therefore, utilization of low-carbon energy resources would be promoted and overall carbon emission would be reduced. Using a fine-grained electricity dispatch model to simulate and optimize the operation of the power system, the carbon emission mitigation potential is quantitatively assessed based on real planning data. The results indicate a significant 10% reduction in CO2 emissions in 2030, amounting to 0.49 Gt. This reduction should be included as an important component for the sector's low-carbon budget. Finally, we assess the potential for further reductions in carbon emissions by making modifications to the planned transmission grid.

The batch and online workload of Internet data centers (IDCs) offer temporal and spatial scheduling flexibility. Given that power generation costs vary over time and location, harnessing the flexibility of IDCs' energy consumption through workload regulation can optimize the power flow within the system. This paper focuses on multi-geographically distributed IDCs managed by an Internet service company (ISC), which are aggregated as a controllable load. The load flexibility resulting from spatial load regulation of online workload is taken into account. A two-step workload scheduling mechanism is adopted, and a computation-power coupling model of ISC is established to facilitate collaborative optimization in active distribution networks (ADNs). To address the model-solving problem based on the assumption of scheduling homogeneity, a model reconstruction method is proposed. An efficient iterative algorithm is designed to solve the reconstructed model. Furthermore, the Nash bargaining solution is employed to coordinate the different optimization objectives of ISC and power system operators, thereby avoiding subjective arbitrariness. Experimental cases based on a 33-node distribution system are designed to verify the effectiveness of the model and algorithm in optimizing ISC's energy consumption and power flow within the system. Paper accepted for IEEE Transactions on Smart Grid. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

Climate change poses a huge threat to human welfare. Hence, developing a low-carbon economy has become a prevailing and inevitable trend. Decarbonization of power generation, especially converting the current power mix into a low-carbon structure, will be a critical option for CO2 emission mitigation. In this paper, an integrated power generation expansion (PGE) planning model towards low-carbon economy is proposed, which properly integrates and formulates the impacts of various low-carbon factors on PGE models. In order to adapt to the characteristics of PGE models based on low-carbon scenario, a compromised modeling approach is presented, which reasonably decreases complexities of the model, while properly keeping the significant elements and maintaining moderate precision degree. In order to illustrate the proposed model and approach, a numerical case is studied based on the background of China's power sector, making decisions on the optimal PGE plans and revealing the prospects and potentials for CO2 emission reduction.

Steady‐state security region (SSR) provides a region‐wise approach to assess the steady‐state security of power systems. Based on the SSR model, we introduce the concept of ‘steady‐state security distance’ (SSD) to provide exact ‘quantitative analysis’ on the scale of security margins for system operators. Then, mathematical models of SSD are formulated. On this basis, implementation of SSD is discussed towards various uncertainties within the power system operation. As the calculation of SSD is in essence a large‐scale non‐linear optimisation process, it requires very long computation time, and thus could not be utilised in practical application. In order to enhance the efficiency of computation, a novel algorithm is proposed, which decomposes the complex optimisation process into two steps: active SSR boundary identification and partial constrained solution. The proposed algorithm remarkably cuts the scale of the optimisation model as well as the number of calculations, thus significantly reduces the computation time to meet requirements of assessment towards day ahead and hours ahead generation schedules. In the end, an IEEE‐30 bus case and a practical case on a provincial power system are both studied to testify the effectiveness of the proposed model and algorithm.

In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system.