The carbon pricing is the main issue of the carbon trading market for enabling cost-effective decarbonization in the energy networks. A nodal carbon pricing model is firstly proposed based on the sharing and integration of the intra-regional carbon emission allowance. In this regard, the game theory is introduced to construct a multi-agent carbon emission allowance bargaining model in this letter. The alternating direction multiplier method is adopted to solve the model considering the competitional burden and privacy-preserving. Numerical results demonstrate that it could significantly reduce the carbon emissions of regional energy networks and improve the economic benefits of prosumers.

This article proposes a new concept of aviation-to-grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using photovoltaics, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced on the basis of the mixed-integer linear programming method to meet electrified aviation charging demand and provide A2G frequency response to the power grid. Case studies are conducted in eight major U.K. airports considering seasonal flight schedules and power system operation scenarios. Results show that the EA charging system can provide effective primary and secondary frequency responses to improve the frequency nadir by 0.2–0.3 Hz under grid disturbance. The total A2G frequency response capacity across the eight U.K. airports can reach 900–1200 MW overnight and 300–900 MW daytime in summer and 1100–1300 MW overnight and 200–700 MW daytime in winter. The annual A2G frequency response revenue is estimated to be £46.58 million, which can cover 19.8% to 30% of EA charging costs.

Light emitting diode (LED) lamps are now an established lighting technology, which is becoming prevalent in all load sectors. However, LED lamps are non-linear electrical loads, and their impact on distribution system voltage quality must be evaluated. This paper provides a detailed analysis of time domain and frequency domain approaches for developing and evaluating models suitable for use in large scale steady-state harmonic power flow analysis of the low frequency (LF) emission of LED lamps. The considered approaches are illustrated using four general categories of LED lamps, which have been shown to cover the vast majority of LED lamps currently available on the market. The aim is an in-depth assessment of the ability of commonly applied models to represent the specific design characteristics of different categories of LED lamps. The accuracy of the models is quantitatively evaluated by means of laboratory tests, numerical simulations, and statistical analyses. This provides an example, for each LED lamp category, of comprehensive information about the overall accuracy that can be achieved in the general framework of large scale LF harmonic penetration studies, particularly in the assessment of voltage quality in low voltage networks and their future evolution.

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.

Consumer electronics such as advanced GPS, vehicular sensors, inertial measurement units (IMUs), and wireless modules integrate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) within internet of thing (IoT), enabling connected autonomous electric vehicles (CAEVs) to optimize energy optimization through eco-driving. In scenarios with traffic light intersections and partial wireless charging lanes (WCL), an eco-driving algorithm must consider net and gross energy consumption, safety, and traffic efficiency. We introduced a deep reinforcement learning (DRL) based eco-driving control approach, employing a twin-delayed deep deterministic policy gradient (TD3) agent for real-time acceleration planning. This approach uses reward functions for acceleration, velocity, safety, and efficiency, incorporating a dynamic velocity range model which not only enables the vehicle to smoothly pass the signalized intersections but also uses partial WCL efficiently and time-adaptively while ensuring traffic efficiency in diverse traffic scenarios. Tested in Simulation of Urban Mobility (SUMO) across various intersections with partial WCL, our method significantly lowered net and gross energy consumption by up to 44.01% and 17.19%, respectively, compared to conventional driving, while adhering to traffic and safety norms.