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This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

• Combined cycle capable of offering three switchable energy supply modes was studied. • The set of the evaporator ensures the effective switching of different cycle modes. • Heat source in low temperature grade helps to desorb the work or basic solution. • Analysis was conducted from both thermodynamic and economic perspectives. • All cycle modes can maintain high comprehensive performance. The emergence of energy-related issues and the growing need for year-round energy supply has created a pressing demand for efficient cogeneration combined cycles. To this end, a new combined cycle using ammonia-water as the working fluid is proposed and studied from both thermodynamic and economic standpoints in this paper. The proposed cycle can effectively operate in power generation, power/refrigeration cogeneration and power/heating cogeneration modes. Parametric analysis and performance optimization of the proposed cycle are carried out, and the genetic algorithm method is employed for the optimization. Under the same initial conditions, the proposed cycle exhibits comprehensive power recovery efficiencies that are 58.11 %, 23.85 %, and 7 % relatively higher than those of the compared cogeneration cycles and Kalina cycle reported in the literature, respectively. The optimization results show that the maximum comprehensive power recovery efficiencies for the power/refrigeration, power/heating and power modes with heat source/cooling water temperature of 250℃/30℃, are 0.2271, 0.2658, and 0.1789, respectively. The associated power/refrigeration, power/heating, and power outputs are 221.1 kW/417.8 kW, 248.5 kW/650.9 kW, and 292.1 kW, respectively. Under these conditions, the overall annual unit cost of comprehensive power and the payback period of the cycle are 0.03947 $/kWh and 3.898 years. Results of the comprehensive analysis indicate that the proposed cycle can achieve satisfactory comprehensive performance for each mode, while also sufficiently recovering sensible heat source. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

With the development of fluid transmission and distribution network in the direction of intelligent pipe network, it is urgent to solve the power supply problem of many instruments in the pipe network. However, solving the problem of power supply for intelligent pipe network systems in remote areas and poor site conditions is a challenging task, and the power supply problem can be solved if the fluid energy in the pipe network can be harvested to power instruments such as sensors. Valve is essential equipment in pipe network system, and installing impeller in their spool can harvest fluid energy for power generation. In this paper, in order to improve the ability of the impeller to harvest fluid energy, the blade profile is optimized by parametrically defined method based on the Isight platform. The energy harvesting under different parameters is obtained through CFD simulation software, the Kriging model is established according to the sample data, and the NSGA-II algorithm is used to optimize. After optimization, the impeller shaft power is increased by 7.22%, the energy harvesting efficiency is increased by 7.50%, and the optimal blade profile is obtained. The ability of the impeller to harvest fluid energy is significantly improved. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic input–output model, in order to present the interaction mechanism between energy supply and demand, and provide optimal design for energy supply–consumption system. Combined with mixed integer programming, the model is able to provide optimal technology development route for the energy supply–consumption system. In this model, the generational change and replacement of energy supply facilities are considered. Based on the model, a case study of China is employed. Three scenarios are designed to compare the different outcome of the model. The optimal objectives of Scenario 1 are Pareto Optimality of minimum total energy consumption and minimum total CO 2 emission. The optimal objective of Scenario 2 is minimum CO 2 emission. The optimal objective of Scenario 3 is minimum energy consumption. Scenario 1 leads to a balancing design of multiple objectives, and the results indicate that energy conservation and CO 2 emission reduction targets can be achieved simultaneously. Compared with the optimal design with single objective of Scenarios 2 and 3, the balancing design with multiple objectives performs better in avoiding over-capacity problems during the modeling period. With well-designed development route, energy intensity can be reduced by about 58%–60%, and CO 2 emission intensity can be reduced by about 34%–36% from 2012 to 2040. The optimal objectives of the model also influence the development of energy demand structure. The modeling results of transport sector are presented as an example in this paper. To achieve the targets of energy conservation and CO 2 emission reduction, both the development routes for energy supply and demand should be well-designed. [ABSTRACT FROM AUTHOR]

Due to increasing amounts of energy being supplied by intermittent renewable energy sources, future energy systems need to be able to react flexibly to sudden changes. To address this challenge, a large body of research is devoted to improving the utilization of energy flexibility of various types of energy resources on the supply and demand side. This is commonly done by applying mathematical models. The objective of this paper is to review such models and identify common modeling approaches used to quantify and optimize the use of flexible energy resources. The literature review is conducted systematically, encompassing 215 publications that feature 694 models of different energy resources. The main distinction of this work, in comparison to other reviews on the topic of energy flexibility lies in its focus on the mathematical formulation of models of energy resources and the quantification of the models' level of detail. The aim is to identify common modeling approaches and the corresponding research problems they address. The review shows that the majority of research efforts are directed towards the management and optimization of distributed energy resources. The most commonly employed modeling approach involves abstract black-box models with limited levels of detail. Linear programming and meta-heuristics are frequently used to optimize energy flexibility. A critical reflection of the current common practice is conducted and recommendations for future research are formulated. [Display omitted] • Literature review of 215 reports presenting model approaches for energy flexibility. • Categories of research problems identified and deduction of two main research focuses. • Presenting models of common energy resources classified into different model types. • Mostly low level of detail seen in modeling energy flexibility. • Optimization models are used to represent and quantify flexibility. [ABSTRACT FROM AUTHOR]

• The structure scheme of hybrid pumping unit is proposed for the first time in this paper. New forms of wind energy can effectively reduce oil field energy consumption by 90%. • The Mechanical-hydraulic energy storage transmission scheme is proposed for the first time. The coupling of wind energy and electric energy can be realized through the new hybrid transmission structure. • A double pump energy supply system is proposed for the first time in this paper. Double pump energy supply system can improve the utilization efficiency of wind energy. Wind energy can replace electric energy to drive pumping units. Wind power generation is used in oil fields, but the energy saving effect is not obvious. This paper proposes a new method of wind energy utilization. Wind energy directly drives pumping unit by new mechanical-hydraulic transmission system without the intermediate conversion into electricity for the first time. This new system can realize hybrid power of wind and electricity and adjust the output energy of the system to meet load requirements. It can effectively address the problems of wind energy utilization and is especially suitable for oil fields in remote areas where electricity is insufficient and its exploitation is difficult. This paper studies the performance of wind-pump system and mechanical-hydraulic system, respectively. Mathematical model is verified by numerical simulation and experiment studies. The economic matching model is established and the parameters of the system are optimized. Finally, this paper puts forward the optimization matching design theory of wind-motor hybrid system. Wind energy utilization rate is higher 60% under optimal parameter combination. [ABSTRACT FROM AUTHOR]

With the development of renewable energy, the number of microgrid systems is gradually increasing, which puts forward higher requirements for the power grid. How to make full use of the internal resources of microgrid, guarantee power supply and relieve power grid's burden is an urgent problem to be solved. In this paper, a planning and optimization strategy of a microgrid cluster consisting of shared hydrogen storage is proposed. This study is mainly divided into three stages. In the first stage, capacity planning and daily operation optimization of energy storage schemes in different scenarios are carried out through a two-layer optimization model. In the second stage, the comprehensive benefits of different scenarios are calculated by comparing whether the microgrids are in a cluster and whether the energy storage is shared. The third stage is to reasonably distribute the benefits of the members of the microgrid cluster. The results show that the complementarity between microgrids and the configuration of hydrogen energy storage can eliminate the phenomenon of power failure, reduce the dependence of microgrids on the superior grid, improve the peak regulation capacity, promote the consumption of renewable energy, and obtain economic benefits. • A microgrid cluster is constructed, realizing the mutual aid of resources. • Reinforcement learning is used for microgrid optimization. • The comprehensive benefits in shared hydrogen storage are calculated. • The benefits are reasonably distributed among participants. [ABSTRACT FROM AUTHOR]

High energy efficiency for low operating cost of desalination can be obtained from operational optimizations as well as component-wise innovations. This paper is concerned with an energy-efficient operation for reverse osmosis (RO) desalination process involving a joint control of multiple modules. Energy resource considered for desalination is of two types: intermittent solar energy and steady grid energy. With solar energy, the energy efficiency is measured by total permeate production obtained while the solar energy is available. With grid energy, the energy efficiency is measured by the power consumed to get unit permeate production rate. Achieving maximum energy efficiency is equivalent to solving constrained optimization. For the constrained optimization, obligatory constraints are given by the permissible ranges of trans-membrane pressure on RO membrane and total dissolved solids of permeate, and optional constraint is given by the permissible range of permeate production rate. Constraints as well as the objective function are modeled by the empirical second order equations in terms of control variables of multiple modules. Jointly optimal values of the control variables are found by the sequential quadratic programming. Experimental results obtained by the jointly optimal control demonstrate superior performances as compared to those acquired by marginally optimal control schemes. [ABSTRACT FROM AUTHOR]

• Optimization-based comparison of natural gas liquefaction processes is proposed. • Broad process flowsheet complexity and natural gas processing scales are considered. • The AP-X process has the lowest energy consumption and cost for 8 MTPA plants. • The single mixed refrigerant process has the highest profit for small-scale plants. • Energetically better processes may not provide a higher economic return. The demand for liquified natural gas grows as it is an energy resource that has more flexible means of transport than non-liquefied natural gas, and is more eco-friendly than other fossil fuels. This paper focused on comparing five different mixed-refrigerant liquefaction processes to determine which one is the most efficient in energetic and economic terms for four different natural gas processing scales (20,000 kg/h and 2, 4, and 8 MTPA of natural gas feed). Simpler processes with one or two refrigerant cycles (single mixed refrigerant, propane pre-cooled mixed refrigerant, and dual mixed refrigerant) and more complex processes with three cycles (mixed fluid cascade and AP-X) were analyzed. All processes were simulated in Aspen HYSYS, and the energy consumption was optimized by a communication between the simulator and an optimization algorithm based on Particle Swarm Optimization coded in MATLAB. The economic analysis was performed using the CAPCOST technique and the rule of six-tenths for upscaling. In the best-optimized scenario, the results show that the most complex process, AP-X, consumes the lowest amount of energy (0.2367 kWh/kg-LNG) while the simplest one, the single mixed refrigerant, consumes the highest value (0.2561 kWh/kg-LNG). In the economic analysis, the results indicate that energetically better processes may not provide a higher economic return even on large scales. [ABSTRACT FROM AUTHOR]

The most important aspect of sustainability in manufacturing is the preservation of energy and natural resources. For modern production, optimized processes that minimize negative impacts on the environment are becoming increasingly important. This can be achieved by increasing energy efficiency through low, clean, and renewable energy consumption. There are many ways to produce less pollution, emissions, and waste in machining: by using more environmentally friendly cooling methods; by applying methods that reduce or eliminate the need for utilization of cooling lubrication; improving the energy efficiency of machining operations; determining the optimal cutting conditions that save resources by increasing machining productivity or reducing the metal removal rate (MRR); minimizing power consumption; and reducing carbon dioxide emissions. This article gives an idea of modern manufacturing with a focus on analyzing the current state of machining operations in terms of saving production resources and ensuring more environmentally friendly production using greener cooling methods of machining such as Dry, Conventional cooling systems, Minimum quantity of lubricant (MQL), Minimum quantity of cooling lubrication (MQCL), Nanofluids, Biodegradable Vegetable Oils, Cryogenic Lubrication, and High-Pressure Cooling (HPC). Finally, the important modern trends of providing resource-saving and environmentally efficient technologies in modern sustainable manufacturing are discussed in this paper. • Present review article deals with the sustainability and resource saving aspects during machining operations. • Different cooling conditions were presented. • Waste minimizing, recycling, pollution etc. were discussed during machining oeprations. [ABSTRACT FROM AUTHOR]

Increasing attention has recently been drawn to energy consumption in manufacturing plants. Facing the challenges from reducing emissions coupled with rising raw material prices and energy costs, manufacturers are trying to balance the energy usage strategy among the total energy consumption, economy, and environment, which can be self-conflicting at times. In this paper, energy systems in manufacturing environments are reviewed, and the current status of onsite energy system and renewable energy usage are discussed. Single objective and multicriteria optimization approaches are effectively formulated for making the best use of energy delivered to the production processes. Energy supply operation suggestions based on the optimization results are obtained. Finally, an example from an automotive assembly manufacturer is described to demonstrate the energy usage in the current manufacturing plants and how the optimization approaches can be applied to satisfy the energy management objectives. According to the optimization results, in an energy oriented operation, it takes 35% more in monetary cost; while in an economy oriented operation, it takes 17% more in megawatt hour energy supply and tends to rely more on the inexpensive renewable energy. [ABSTRACT FROM AUTHOR]

Increasing attention has recently been drawn to energy consumption in manufacturing plants. Facing the challenges from reducing emissions coupled with rising raw material prices and energy costs, manufacturers are trying to balance the energy usage strategy among the total energy consumption, economy, and environment, which can be self-conflicting at times. In this paper, energy systems in manufacturing environments are reviewed, and the current status of onsite energy system and renewable energy usage are discussed. Single objective and multicriteria optimization approaches are effectively formulated for making the best use of energy delivered to the production processes. Energy supply operation suggestions based on the optimization results are obtained. Finally, an example from an automotive assembly manufacturer is described to demonstrate the energy usage in the current manufacturing plants and how the optimization approaches can be applied to satisfy the energy management objectives. According to the optimization results, in an energy oriented operation, it takes 35% more in monetary cost; while in an economy oriented operation, it takes 17% more in megawatt hour energy supply and tends to rely more on the inexpensive renewable energy. [ABSTRACT FROM AUTHOR]

In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer's selling price. A higher selling price increases retailers' profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer's energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower sub-problem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer's acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed. [ABSTRACT FROM AUTHOR]

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. • Three-step multi-period pinch-based methodology for energy planning is presented. • The model incorporates an actual billing system. • Time-sliced based optimization model is developed in the proposed framework. • Optimal emission reduction ratio and storage size are identified as 0.71 and 236 kWh. • The total electricity bill can be further reduced by 35%. [ABSTRACT FROM AUTHOR]

Abstract: Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller. [Copyright &y& Elsevier]

Abstract: Smart grids utilise omnidirectional data transfer to operate a network of energy resources. Associated technologies present operators with greater control over system elements and more detailed information on the system state. While these features may improve the theoretical optimal operating performance, determining the optimal operating strategy becomes more difficult. In this paper, we show how a decision tree ensemble or ‘forest’ can produce a near-optimal control strategy in real time. The approach substitutes the decision forest for the simulation–optimisation sub-routine commonly employed in receding horizon controllers. The method is demonstrated on a small and a large network, and compared to controllers employing particle swarm optimisation and evolutionary strategies. For the smaller network the proposed method performs comparably in terms of total energy usage, but delivers a greater demand deficit. On the larger network the proposed method is superior with respect to all measures. We conclude that the method is useful when the time required to evaluate possible strategies via simulation is high. [Copyright &y& Elsevier]

Abstract: Coalbed methane (CBM) is an important global energy resource and liquefaction is suggested to best utilize it. Different from ordinary natural gas, CBM usually contains a high proportion of nitrogen, which cannot be removed by purification procedures applied in ordinary natural gas liquefaction processes. One approach for separating nitrogen from CBM is by distillation after liquefaction. In this way, nitrogen is liquefied together with methane, and the liquefaction system performance may change along with the nitrogen content of CBM feed gas. The liquefaction process adopting nitrogen expansion with propane pre-cooling is usually considered suitable for small-scale liquefaction plants due to its simplicity and is the focus of this paper. Taking the unit product liquefaction power consumption as the major index for analysis, optimum parameters of the liquefaction process for CBM feed gas containing different nitrogen contents are calculated. Based on the optimization results, the effects of nitrogen content as well as the other two important technical indexes (liquefaction rate and methane recovery rate) on system performance are also investigated. [Copyright &y& Elsevier]

Abstract: Sensitivity analysis of energy demands is carried out in this paper to study their influence on performance of CCHP system. Energy demand is a very important and complex factor in the optimization model of CCHP system. Average, uncertainty and historical peaks are adopted to describe energy demands. The mix-integer nonlinear programming model (MINLP) which can reflect the three aspects of energy demands is established. Numerical studies are carried out based on energy demands of a hotel and a hospital. The influence of average, uncertainty and peaks of energy demands on optimal facility scheme and economic advantages of CCHP system are investigated. The optimization results show that the optimal GT’s capacity and economy of CCHP system mainly lie on the average energy demands. Sum of capacities of GB and HE is equal to historical heating demand peaks, and sum of capacities of AR and ER are equal to historical cooling demand peaks. Maximum of PG is sensitive with historical peaks of energy demands and not influenced by uncertainty of energy demands, while the corresponding influence on DH is adverse. [Copyright &y& Elsevier]

Abstract: This paper describes research into the optimal operation of building heating, ventilation, and air-conditioning (HVAC) systems focusing on both temperature and humidity control. While most previous work on HVAC optimization has been limited to evaluation of conventional temperature-based control systems, this study emphasizes the humidity control issue in meeting both sensible and latent building loads. The analysis is based on a combination of a realistic simulation of a direct expansion (DX) air-conditioning system and a direct-search numerical optimization technique. The simulation models have been validated through comparisons with field data. Optimization was performed on five different system control variables to minimize system power consumption while meeting building loads and maintaining comfort. Indoor temperature and humidity are also optimized within standard comfort constraints. Building loads were modeled using an extended bin method that allows consideration of the interactions between loads and indoor conditions. Results indicate that minimum energy use typically occurs at low airflow rates, with indoor humidity levels below the upper comfort limit. Results also show that coil air bypass and evaporator circuiting control are typically not necessary unless operation would otherwise result in overcooling. The optimization results also translate to relatively simple strategies for system control. Significant savings are demonstrated over conventional control strategies used in packaged DX equipment. [Copyright &y& Elsevier]

• A joint framework with P2P energy sharing and dynamic network structure is proposed. • A dynamic network structure model affected by the P2P energy sharing is provided. • A solution algorithm with matching processes and branch-exchange is designed. • The effectiveness of the proposed framework is demonstrated. The integration of distributed energy resources facilitates peer-to-peer (P2P) energy sharing as an effective way to coordinate the energy scheduling. Previous research has focused on economic P2P energy sharing of user side without considering the possible response strategies of network sides. This paper proposes a P2P energy sharing framework that takes into consideration the dynamic network structure. A P2P energy sharing model aimed at increasing the energy local consumption and reducing each prosumers' power losses arisen from P2P energy sharing is built for the P2P energy schedule. In the physical network, a dynamic network structure model is designed to incorporate the network operator into the energy sharing process, and obtain the better network structure while reducing the power losses of whole network. These two proposed models are jointly optimized by the upper and lower layer to get the optimal P2P energy sharing schedule, network operations conditions and comprehensive energy utilization. The solution algorithm for the joint optimization is composed of a designed matching mechanism and branch-exchange method and realized by the iteration process. Finally, numerical analysis reveals the effectiveness of the proposed framework in terms of prosumers' strategies, network structures, comprehensive energy utilization, and practical feasibility. [ABSTRACT FROM AUTHOR]