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Offshore wind power stands out as a promising renewable energy source, offering substantial potential for achieving low carbon emissions and enhancing energy security. Despite its potential, the expansion of offshore wind power faces considerable constraints in offshore power transmission. Hydrogen production derived from offshore wind power emerges as an efficient solution to overcome these limitations and effectively transport energy. This study systematically devises diverse hydrogen energy supply chains tailored to the demands of the transportation and chemical industries, meticulously assessing the levelized cost of hydrogen (LCOH). Our findings reveal that the most cost-efficient means of transporting hydrogen to the mainland is through pipelines, particularly when the baseline distance is 50 km and the baseline electricity price is 0.05 USD/kWh. Notably, delivering hydrogen directly to the port via pipelines for chemical industries proves considerably more economical than distributing it to hydrogen refueling stations, with a minimal cost of 3.6 USD/kg. Additionally, we assessed the levelized cost of hydrogen (LCOH) for supply chains that transmit electricity to ports via submarine cables before hydrogen production and subsequent distribution to chemical plants. In comparison to offshore hydrogen production routes, these routes exhibit higher costs and reduced competitiveness. Finally, a sensitivity analysis was undertaken to scrutinize the impact of delivery distance and electricity prices on LCOH. The outcomes underscore the acute sensitivity of LCOH to power prices, highlighting the potential for substantial reductions in hydrogen prices through concerted efforts to lower electricity costs. [ABSTRACT FROM AUTHOR]

It is significant to develop a green ammonia preparation method for decreasing carbon emission. This work demonstrates a zero-carbon method for preparing ammonia feedstock consisting of a ratio of H 2 :N 2 ≥ 3:1 by direct electrolysis of air and steam as fuel gas through solid oxide electrolysis cell (SOEC). Through adjusting the working current and proportion of air/steam, the ratio of H 2 and N 2 can be achieved to more than 4:1, and the fuel is pure enough to be used for synthesizing ammonia. The production rate of ammonia feedstock reaches 1.79 × 10−6 mol/ (cm2·s), and the energy conversion efficiency (ECE) reaches 69.30 %. A stable 100 h electrolysis operation is carried out to generate H 2 /N 2 required for ammonia production. This work provides a new efficient method for the synthesis of green ammonia. • One-step method to produce feedstock for green ammonia is developed. • H 2 :N 2 ≥ 3:1 is achieved by direct electrolysis of air/steam through solid oxide cells. • The ammonia production rate reached to 1.79 × 10−6 mol/ (cm2·s). • A stable operation is observed for 100 h as required for ammonia production. • Partial Ni oxidation near the fuel inlet is the main cause of cell degradation. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive solution methodology for the pricing difficulty when Lagrange multipliers are not unique. A linear optimization model is proposed to solve the congestion-related pricing difficulty. The objective function of the model is set to be minimizing the congestion surplus. In addition, an incentive-based allocation approach is incorporated in the solution procedure for cases when no marginal participant exists. The unique pricing solution obtained through our methodology can achieve proper reallocation of the undetermined surplus. Further, we discuss the reference bus independence property of the proposed pricing methodology. Numerical results are provided to fully test the proposed methodology. Other possible solutions are also presented for comparison. [ABSTRACT FROM PUBLISHER]

Summary: With a high proportion of variable renewable energy integration, accurate forecasting approach is of vital importance in ensuring the reliable and economic operation of power system. Therefore, in this article, a novel method for predicting photovoltaic (PV) power generation based on convolutional neural network (CNN) is proposed. Analytical models of PV systems are formulated, thereby providing physical knowledge about the relationship between PV output and critical meteorological features. To explore the nonlinear and time‐varying properties of PV output, CNN is adopted in this article, which matches the patterns of similar days. Case studies based on realistic datasets in Australia demonstrate that the forecasting performance for solar power can be effectively improved by taking advantage of the proposed CNN‐based learning method. [ABSTRACT FROM AUTHOR]

According to the energy policy, which encourages local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers is proved to be a more effective way compared with independent operations of each prosumer. In this paper, an energy storage (ES)-equipped energy-sharing provider (ESP) is proposed to facilitate the energy sharing of multiple PV prosumers. With the help of the ESP, the autonomous PV prosumers can be formed as an energy-sharing network, and the energy-sharing activities can be categorized as direct sharing and buffered sharing. First, with the assistance of the ES, a day-ahead scheduling model of the ESP is built to increase the operation profit and improve the net power profile of the energy-sharing network, which considers the uncertainty of PV energy, electricity prices, and prosumers’ load via stochastic programming. Moreover, to further increase the energy sharing, a real-time demand response model based on a Stackelberg game is presented to coordinate the energy consumption behavior of prosumers by using internal prices. Finally, through a practical case study, the effectiveness of the method is verified in terms of improving the economic benefits and PV energy sharing. [ABSTRACT FROM PUBLISHER]

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy. [ABSTRACT FROM PUBLISHER]

In a smart grid environment, the joint operation of multiple Smart Buildings (SBs) could be more advantageous than the independent operation of each individual SB. In order to enable the joint operation, the concept of Smart Building Cluster (SBC) is introduced in this paper. An energy management framework for achieving optimal operations of SBC is proposed, and the information exchange processes between the Smart Building Cluster Operator (SBCO) and the participating SBs are described. A multi-party energy management model for SBC based on non-cooperative game theory is proposed, considering building-integrated PV systems and automatic demand response (ADR), with all participating SBs viewed as players in the game. The existence of Nash equilibrium in the game model is proved, and the process for solving the Nash equilibrium strategy is modeled as a multi-objective optimization problem (MOP). Furthermore, the solution method and procedure based on an iterative method are proposed. Finally, via a practical example, the effectiveness of the model is verified. The proposed method is able to reduce the total cost of the SBs by 4.6% and improve the load factor of the SBC from 0.68 to 0.76 when the proportion of shiftable loads is 25% in the total load profile. [ABSTRACT FROM AUTHOR]

Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate. [ABSTRACT FROM PUBLISHER]