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With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

Global warming and climate change are becoming a global concern. In this regard, international agreements and initiatives have been launched to accelerate the use of renewable energy and to mitigate greenhouse gas (GHG) emissions. Yemen is one of the countries signed on these agreements. However, Yemen is facing the problem that the structure of the power grid is fragile and the power shortage is serious. Accordingly, this paper aims to study the potential for renewable energy in Yemen and assess the technical and economic feasibility of hybrid energy systems. Firstly, this paper introduces the status and challenges of Yemen&rsquo, s electricity sector, the status of renewable energy, and the status of GHG emission. Secondly, this study proposes the method of optimizing different configurations of off-grid hybrid (solar/wind/diesel engine) energy systems for electrifying various consumers in Taiz province, Yemen under three scenarios of energy strategies. The objective function is to seek the most optimal hybrid energy system that achieves the least cost and most advantageous technical performance, while instigating the best economic scenario of energy strategies. Finally, Homer pro software is used for simulation, optimization, and sensitivity analysis of the designed energy systems. The results found the best economically feasible scenario, the hybrid PV/wind/diesel energy system, among the other scenarios. A photovoltaic (PV)/wind energy system achieved the best technical performances of 100% CO2 reduction, with a 54.82% reduction in the net present cost (NPC) and cost of energy (COE), while the hybrid energy system (PV/wind/diesel engine) achieved the best economic cost of 61.95% reduction in NPC and COE, with a 97.44% reduction of CO2 emission.

Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a distributed energy system based on blockchains allows any node in the whole network to read data. In many application scenarios, user privacy cannot be effectively protected, and there is a security problem that the attack cannot be traced. In this paper, we propose an energy management mode based on a permissioned blockchain for a renewable energy microgrid. The novel permissioned blockchain framework uses entity mapping with a unique identity for each enterprise, natural person, or device, in order to avoid ineligible participants to join the microgrid. Each peer entity keeps the transaction information index of the whole network, but only keeps its own specific transaction information, so they can retrieve the transaction information of other peer entities but cannot obtain the details without permission. Moreover, this model could avoid communication delays and promote plug-and-play due to the distributed nature of the permissioned blockchain. The performance of the proposed method is evaluated with a demonstration program which is designed and deployed on a Hyperledger Fabric permissioned blockchain. Simulation results show the feasibility of the proposed method, and the model is conducive to the protection privacy and P2P energy management for decentralized energy systems.

Solar energy has attracted the attention of researchers around the world due to its advantages. However, photovoltaic (PV) panels still have not attained the desired efficiency and economic mature. PV tracking techniques can play a vital role in improving the performance of the PV system. The aim of this paper is to evaluate and compare the technical and economic performance of grid-connected hybrid energy systems including PV and fuel cells (FC) by applying major types of PV tracking technique. The topology and design principles and technical description of hybrid system components are proposed in this paper. Moreover, this paper also introduces economic criteria, which are used to evaluate the economy of different PV tracking techniques and seek the optimal configuration of system components. In the case study, the results show that the vertical single axis tracker was ranked 1st in terms of highest PV generation, penetration of renewable energy to the grid, lowest CO2 emission, highest energy sold to the grid and lowest purchased, and lowest net present cost (NPC) and levelized cost of energy (LCOE). The study found that the optimal design of a grid-connected hybrid energy system (PV-FC) was by using a vertical single axis tracker which has the lowest NPC, LCOE.

The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. Thus, in this paper, we propose a dynamic adaptive cross-chain trading mode for multi-microgrid joint operation. The novelty is to design a proof of credit threshold consensus mechanism to achieve effective information verification. This consensus mechanism can ensure the adaptive consistency of cross-chain information without changing the existing blockchain architecture of each system. At the same time, we design a corresponding key management interoperability protocol based on RSA algorithm and Chinese remainder theorem, which can realize data transfer and information consensus for cross-chain transactions. The theoretical analysis verifies that the cross-chain communication information is effective and the system is able to protect against the attack of malicious nodes. Finally, a cross-chain simulation experiment is established to analyze the operation efficiency. The result shows that this cross-chain trading takes place within seconds, which basically meets the response requirements for multi-microgrid joint operation.

With the increase of the penetration rate of photovoltaic (PV) power plant in the power system, PV power fluctuation has become one of the important factors affecting the power quality. The energy storage system (ESS) is an effective way to smooth short-term PV power fluctuation and has been widely used. The control strategy is a key factor that will influence the smoothing effect and size of ESS. In this paper, by using ESS to smooth PV power fluctuation, we proposed a novel control strategy that can regulate the state of charge (SoC) of the battery and calculate the output power of ESS based on the super short-term prediction of PV power. Besides, the rolling prediction theory is introduced into the control strategy, which makes the regulating process be implemented at every sampling moment. Based on this control strategy, an optimal sizing model of ESS is built to analyze the economy of the combined system. Finally, actual data of a PV power plant are used for simulation and verification. The results demonstrate that the proposed strategy can smooth the PV power fluctuation effectively. The total cost and the power of exceeding limitation both are reduced.

Due to their intermittent nature, the use of renewable energy sources has faced the challenges of power insecurity and low efficiency. Recently, fuel cells (FCs) have become a potential choice for backup-power generation in remote microgrids due to their reduced maintenance needs and long lifecycle. However, the efficiency of hydrogen FCs as backup power needs to be improved. The efficiency is related to system sizing and operational techniques. Thus, this paper proposes an efficient energy management strategy and optimal configuration models based on a hybrid system including photovoltaics (PVs) and hydrogen FCs to achieve a high operational efficiency and optimize the system configuration. The system model is built, and an energy management strategy is proposed first. This strategy determines when the PV and FC supply power to a load under various conditions. Proper sizing of the sub-components in the PV/FC hybrid system also plays an essential role in ensuring continuous power flow. To ensure power quality, the hybrid system configuration is estimated using a systematic method without affecting the power quality to minimize residual power losses. In addition, various essential simulations are performed to verify the proposed model. The results indicate that the total efficiency of the system can reach 47.9%.

Energy trading in the multi-energy market is affected by many uncertainties, especially the fluctuation of renewable energy sources and intermittent demand behavior of customers. The real-time energy management can effectively solve the impact of various uncertainties, ensure the instantaneous balance of energy and improve trading returns. A bidding strategy for multi-energy market is presented, in which reserve price adjustment and dynamic compensation mechanism is innovatively integrated into adaptive learning process. All energy trading participants conduct adaptive learning bidding adjustment based on real-time information in order to obtain higher transaction rate and transaction income. Meanwhile, adding dynamic compensation to the quoted price of fossil energy increases the consumption rate of renewable energy and reduces the emissions of pollutants. Furthermore, blockchain technology can ensure the seamless and effective performance of the presented bidding strategy. In the case study, the results show that our bidding strategy has an obvious advantage in social welfare and allocation efficiency than existing bidding strategies. Moreover, the problem of environmental pollution can be solved to a certain extent through flexible dynamic compensation. Finally, a decentralized application of blockchain is developed to demonstrate how the system could realize real-time energy management and dynamic trading in practice.