• Energy Research

Under the downward tendency of prices of renewable energy generators and upward trend of hydrogen demand, this paper studies the technoeconomic supplement of P2G clusters with hydrogen pipeline for HVDC to jointly consume renewable energy. First, the planning and operation constraints of large-capacity P2G clusters is established. On this basis, the multistage coordinated planning model of renewable energy, HVDCs, P2Gs and hydrogen pipelines is proposed considering both variability and uncertainty, rendering a distributionally robust chance-constrained (DRCC) program. Then this model is applied in the case study based on the real Inner Mongolia-Shandong system. Compared with energy transmission via HVDC only, P2G can provide operation supplement with its operational flexibility and long term economic supplement with increasing demand in high-valued transportation sector, which stimulates an extra 24 GW renewable energy exploration. Sensitivity analysis for both technical and economic factors further verifies the advantages of P2G in the presence of high variability due to renewable energy and downward tendency of prices of renewable energy generators. However, since the additional levelized cost of the P2G (0.04 RMB/kWh) is approximately twice the HVDC (0.02 RMB/kWh), P2G is more sensitive to uncertainty from both renewable energy and hydrogen demand.

Under the downward tendency of prices of renewable energy generators and upward trend of hydrogen demand, this paper studies the technoeconomic supplement of P2G clusters with hydrogen pipeline for HVDC to jointly consume renewable energy. First, the planning and operation constraints of large-capacity P2G clusters is established. On this basis, the multistage coordinated planning model of renewable energy, HVDCs, P2Gs and hydrogen pipelines is proposed considering both variability and uncertainty, rendering a distributionally robust chance-constrained (DRCC) program. Then this model is applied in the case study based on the real Inner Mongolia-Shandong system. Compared with energy transmission via HVDC only, P2G can provide operation supplement with its operational flexibility and long term economic supplement with increasing demand in high-valued transportation sector, which stimulates an extra 24 GW renewable energy exploration. Sensitivity analysis for both technical and economic factors further verifies the advantages of P2G in the presence of high variability due to renewable energy and downward tendency of prices of renewable energy generators. However, since the additional levelized cost of the P2G (0.04 RMB/kWh) is approximately twice the HVDC (0.02 RMB/kWh), P2G is more sensitive to uncertainty from both renewable energy and hydrogen demand.

With the increasing penetration of distributed energy resources such as photovoltaics (PVs), frequent voltage and congestion problems are expected to occur in distribution grids. Existing solution approaches based on centralized or distributed offline optimization cannot handle the dynamics of distribution grids in real time, i.e., by the time the computations are finished, the used data are already outdated as the grid loadings evolve so fast. Moreover, the unfair distribution of costs or burdens among end-users to relieve network issues, for example with PV curtailment, has not been sufficiently considered either. To address these issues, a fairness-incorporated online feedback optimization (OFO) approach is proposed. This approach produces implementable intermediate iterates for inverter-based assets using online optimization and suitably integrated measurements, and thus can work with the distribution grid dynamics. Moreover, it addresses the unfairness issue by modifying the voltage sensitivity matrices and leverages the feedback-based nature of OFO to compensate for the resulting modeling inaccuracies. Case studies based on a 96-bus low-voltage grid demonstrate the effectiveness of the fairness-incorporated OFO to handle the distribution grid dynamics and improve fairness, in both static and dynamic cases, with balanced and unbalanced grid models.

With the increasing penetration of distributed energy resources such as photovoltaics (PVs), frequent voltage and congestion problems are expected to occur in distribution grids. Existing solution approaches based on centralized or distributed offline optimization cannot handle the dynamics of distribution grids in real time, i.e., by the time the computations are finished, the used data are already outdated as the grid loadings evolve so fast. Moreover, the unfair distribution of costs or burdens among end-users to relieve network issues, for example with PV curtailment, has not been sufficiently considered either. To address these issues, a fairness-incorporated online feedback optimization (OFO) approach is proposed. This approach produces implementable intermediate iterates for inverter-based assets using online optimization and suitably integrated measurements, and thus can work with the distribution grid dynamics. Moreover, it addresses the unfairness issue by modifying the voltage sensitivity matrices and leverages the feedback-based nature of OFO to compensate for the resulting modeling inaccuracies. Case studies based on a 96-bus low-voltage grid demonstrate the effectiveness of the fairness-incorporated OFO to handle the distribution grid dynamics and improve fairness, in both static and dynamic cases, with balanced and unbalanced grid models.

With the increasing penetration of distributed energy resources (DERs), prosumers are becoming more active and consumer-centric electricity market designs are needed at the distribution grid level. In order to co-optimize the local energy source management of the prosumers and the energy trading between them, an integrated hybrid local electricity market (LEM), which combines peer-to-peer (P2P) markets and community-based markets, is designed. Moreover, the integrated LEMs are decomposed into two market structures, namely the distributed structure and the hierarchical structure, leveraging the alternating direction method of multipliers (ADMM). In the distributed structure, the integrated hybrid LEM is decomposed into multiple blocks by agents who coordinate a community-based market and negotiate with other agents for the P2P trading. In the hierarchical structure, the integrated hybrid LEM is decomposed into two blocks by markets with a centralized P2P market and several community-based markets. Simulation results suggest that the hierarchical structure converges to the optimal result for both the locally adaptive ADMM (LA-ADMM) and constant penalty factor ADMM with various values. The distributed structure achieves a distributed P2P market, but it only converges with LA-ADMM and exhibits a substantially higher computational burden than the hierarchical structure due to its multi-block nature.

With the increasing penetration of distributed energy resources (DERs), prosumers are becoming more active and consumer-centric electricity market designs are needed at the distribution grid level. In order to co-optimize the local energy source management of the prosumers and the energy trading between them, an integrated hybrid local electricity market (LEM), which combines peer-to-peer (P2P) markets and community-based markets, is designed. Moreover, the integrated LEMs are decomposed into two market structures, namely the distributed structure and the hierarchical structure, leveraging the alternating direction method of multipliers (ADMM). In the distributed structure, the integrated hybrid LEM is decomposed into multiple blocks by agents who coordinate a community-based market and negotiate with other agents for the P2P trading. In the hierarchical structure, the integrated hybrid LEM is decomposed into two blocks by markets with a centralized P2P market and several community-based markets. Simulation results suggest that the hierarchical structure converges to the optimal result for both the locally adaptive ADMM (LA-ADMM) and constant penalty factor ADMM with various values. The distributed structure achieves a distributed P2P market, but it only converges with LA-ADMM and exhibits a substantially higher computational burden than the hierarchical structure due to its multi-block nature.

Abstract To be competitive in the electricity markets, various technologies have been reported to increase profits of wind farm owners. Combining battery storage system, wind farms can be operated as conventional power plants which promotes the integration of wind power into the power grid. However, high expenses on batteries keep investors away. Retired EV batteries, fortunately, still have enough capacity to be reused and could be obtained at a low price. In this work, a two-stage optimization of a wind energy retired EV battery-storage system is proposed. The economic performance of the proposed system is examined concerning its participation in the frequency containment normal operation reserve (FCR-N) market and the spot market simultaneously. To account uncertainties in the wind farm output, various electricity market prices, and up/down regulation status, a scenario-based stochastic programming method is used. The sizing of the equipment is optimized on top of daily operations of the hybrid system which formulates a mixed-integer linear programming (MILP) problem. Scenarios are generated with the Monte Carlo simulation (MCS) and Roulette Wheel Mechanism (RWM), which are further reduced with the simultaneous backward method (SBM) to increase computational efficiency. A 21 MW wind farm is selected as a case study. The optimization results show that by integrating with a retired EV battery-storage system (RESS) and a bi-directional inverter, the wind farm can increase its profits significantly when forwarding bids in both of the aforementioned electricity markets.

Abstract To be competitive in the electricity markets, various technologies have been reported to increase profits of wind farm owners. Combining battery storage system, wind farms can be operated as conventional power plants which promotes the integration of wind power into the power grid. However, high expenses on batteries keep investors away. Retired EV batteries, fortunately, still have enough capacity to be reused and could be obtained at a low price. In this work, a two-stage optimization of a wind energy retired EV battery-storage system is proposed. The economic performance of the proposed system is examined concerning its participation in the frequency containment normal operation reserve (FCR-N) market and the spot market simultaneously. To account uncertainties in the wind farm output, various electricity market prices, and up/down regulation status, a scenario-based stochastic programming method is used. The sizing of the equipment is optimized on top of daily operations of the hybrid system which formulates a mixed-integer linear programming (MILP) problem. Scenarios are generated with the Monte Carlo simulation (MCS) and Roulette Wheel Mechanism (RWM), which are further reduced with the simultaneous backward method (SBM) to increase computational efficiency. A 21 MW wind farm is selected as a case study. The optimization results show that by integrating with a retired EV battery-storage system (RESS) and a bi-directional inverter, the wind farm can increase its profits significantly when forwarding bids in both of the aforementioned electricity markets.