• Energy Research

AbstractIn this study, an intelligent energy management method is introduced to deal with the hydrogen‐dominant hybrid energy system with low carbon consideration. Specially, both the new type fuel cell, solid oxide fuel cell, and chemical battery are subtly modelled to construct a high‐efficient hybrid energy system, in which the thermodynamics feature and accurate battery model characteristics, as well as low carbon effect, are considered. Because the hybrid energy system incorporates various complex dynamic operation features that are hard to capture via conventional operation strategy, an energy management method based on deep reinforcement learning techniques is proposed to guide the intelligent operation with self‐adaptive performance. In the simulation, it is observed that highly efficient use of hydrogen in the hybrid energy system with the aid of chemical battery could achieve good economic benefit, as well as low carbon advantages. Powered by the gas and chemical energy coupling storage effect and state‐of‐the‐art machine learning methods, the proposed intelligent energy management strategy can benefit more renewable energy adoption and guarantee the ultimate environmental friendly low carbon ecosystem in the long‐term future.

Abstract A novel flexible energy-use mechanism is proposed based on the energy coordination among electrolyzers, steam methane reforming (SMR) plants and gas-fired units in the day-ahead scheduling of the integrated gas-electrical system. The power-to-gas (P2G) energy conversion method provides an effective solution to the energy dilemma in China. Nevertheless, this method requires a high-value chain for application due to its high investment cost at present. Producing, and then selling, hydrogen via electrolyzers is more valuable than storing it for a later generation. The two hydrogen production methods of SMR and electrolysis can be combined with the operation optimization of electrical and natural gas systems to form a flexible energy-use mechanism. A model of the flexible energy-use mechanism is established. The model can be integrated into interdependent natural gas and electricity systems, aiming to alleviate the shortage of natural gas, improve energy efficiency and lower carbon emissions. To demonstrate the economic feasibility and carbon dioxide emissions reduction of the proposed mechanism, a bus-6 power system integrated with a node-6 natural gas system and an IEEE 30-bus system with a modified Belgian 20-node gas system are examined. It is concluded that the proposed flexible energy-use mechanism in the power and natural gas systems has higher economic value and lower carbon emissions than the power-to-hydrogen-to-methane-to-pipeline path.

An incentive-compatible demand response (DR) strategy is proposed for engaging spatially-coupled Internet data center (IDC) and their spatial load regulation potentials in electricity markets. First, an optimal power flow (OPF) model which considers IDC (referred to as IOPF) is proposed to coordinate IDC DRs and power system operations, in which the formulated IDC load model is compatible with the conventional OPF model. Second, the IOPF-based locational marginal price (LMP) (referred to as ILMP) is derived, which is used to analyze the impact of spatially-coupled DR options on ILMPs and the IDC DR’s clearing price. Third, IDC DR activation strategy is proposed as an extension of Net Benefit Test (NBT), where the risk of negative benefit to IDCs is derived. An IDC DR’s activation strategy is proposed based on NBT to determine whether non-zero IDC DR dispatches in IOPF are cost-effective. Last, an extra benefit redistribution mechanism is proposed to achieve the incentive-compatibility between social welfare and IDC benefit. The proposed approach is based on the Vickrey–Clarke–Groves mechanism and the contribution factor theory, where the market’s revenue adequacy is maintained, and benefits to IDCs and other customers are guaranteed. Simulation results verify the efficiency of the proposed method, implying that the proposed spatially-coupled DRs are compatible with and can enhance existing DR mechanisms.

Thermostatically controlled loads (TCLs) have demonstrated their potentials in demand response. One of the key challenges for TCLs to be integrated into the system-level operation is building a compact aggregated model, in which the TCL primary behaviors are accurately captured. In this paper, TCLs are aggregated as a virtual generator and two batteries according to their different compressor types and control methods for smoothing out multi-time-scale variability of wind power generation. This will bring system operator great convenience to manage TCLs and conventional components when the system-level decisions are made. Accordingly, accurate parameters of virtual generator and batteries are critical to effectively coordinate TCLs with other resources in the system operation. However, it tends to be difficult to obtain such aggregated parameters as a result of insufficient data for each TCL. To address this problem, high-dimensional model representation (HDMR) is introduced to estimate the aggregated parameters of virtual generator and batteries using the probability distribution of TCL parameters. A numerical simulation study demonstrates that aggregated parameters of virtual generator and batteries can be accurately estimated by HDMR. And virtual generator and batteries are able to follow actual behaviors of TCL populations in power system operations.

This paper initially proposes a real options model for the investment of the Power-to-gas (P2G) plant based on uncertain operating cost which mainly refers to the price of electricity. Through the analysis of the uncertainty parameters affecting the operation of the P2G project, the mathematical model expressing the relations between the parameters of P2G operation cost, electricity price, sunk cost, and other parameters are established. The Brownian motion is utilized to describe the operation cost, based on which, the option value and the project value models of P2G are derived in detail. According to these two models, the optimal investment timing of the P2G device and the corresponding optimal investment capacity can be determined. The above models are verified by numerical simulation. In addition, the influence of the change of parameters on the investment timing and investment capacity in the real options model is studied. The results show that the volatility of electricity price has a greater impact on the option value of P2G project than that of other parameters. When there is a high operating cost uncertainty, waiting is a better option, and the investment can be performed when the operating cost falls to the cost with reference to the optimal investment timing.

The local energy market (LEM) is a promising solution to address the imbalance problem caused by the increasing renewable energy. The network costs can serve as a price signal to guide nearby trading. However, the security-constrained economic dispatch method with large-scale prosumers has a lower calculation efficiency which fails to meet the high-frequency clearing needs of the LEM. Therefore, this paper proposes a fast sequential clearing method for congestion-aware local energy and flexibility markets. The LEM is designed based on continuous double auctions (CDA) to clear the market quickly. During CDA, network losses, and transmission fees are dynamically calculated based on AC power flow sensitivity factors and added to the bids/offers. To address congestion issues and prevent transaction cancellations, a local flexibility market (LFM) is established to utilize prosumers’ flexible capacity. The Market clearing efficiency of LFM is improved by adopting the fictitious nodal demand (FND) model and linear approximation of inscribed regular polygon constraint. The improved CDA in LEM promotes local transactions and enables effective network cost recovery. The LFM expands the total transaction volume by employing local flexibility. Case studies validate that the proposed methodology improves clearing efficiency, benefits prosumers, and ensures the operational security of distribution networks.

The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-topeer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP's contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources.

Integrated energy systems (IESs) considering power-to-gas (PtG) technology are an encouraging approach to improve the efficiency, reliability, and elasticity of the system. As the evolution towards decarbonization is increasing, the unified coordination between IESs and PtG technology is also increasing. PtG technology is an option for long-term energy storage in the form of gas, but, compared to other technologies, it is economically expensive at the present time to optimize the technology. This article presents a comprehensive review of the state-of-the-art research and of the developments regarding integrated energy systems considering PtG technology. This presented review emphasizes planning and economic analysis, including system integration enhancements focusing on optimization, conversion technologies, and energy storage to improve the operation and stability and to enhance the facilities for consumers. The role of a PtG system in generation, transmission, distribution, and consumption is discussed. By emphasizing planning, integration, and the role, this paper aims to guide researchers, scientists, engineers, and policy makers towards effective research and broad strategies that sustain an IES-PtG.

To address microgrid tie flow errors caused by wind generation variability, this paper proposes and develops a multi-time scale coordinated control and scheduling strategy for inverter-based thermostatically controlled loads (TCLs). First, in hour-time scale, inverter-based TCLs with adjusting temperature set-point are modeled as virtual generators to compensate tie flow deviations in the day-ahead plan. Next, in minute-time scale, virtual batteries representing operating behaviors of inverter -based TCLs with frequency control are scheduled determined by the control of virtual generators in hour-time scale. The virtual batteries are scheduled to smooth out tie flow errors corresponding to day-ahead plan and hour-time scale schedules. The multi-time scale control methods are coordinated to employ the response potential of inverter-based TCLs and response curve-based methods are proposed to control inverter-based TCLs considering the customer privacy. The multi-time scale stochastic schedules which are based on response curves of inverter-based TCLs are coordinated to accommodate wind generation variability. Simulation results demonstrate that the microgrid tie flow errors are effectively mitigated by the proposed multi-time scale coordinated control and scheduling of inverter -based TCLs.