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Optimally operating an integrated electricity-gas system (IEGS) is significant for the energy sector. However, the IEGS operation model's nonconvexity makes it challenging to solve the optimal dispatch problem in the IEGS. This letter proposes a new Benders decomposition-based (IBD) algorithm catering to a commonly used steady-state dispatch model of the IEGS. This IBD algorithm leverages a refined decomposition structure where the subproblems are linear and ready to be solved in parallel. We analytically compare our IBD algorithm with an existing Benders decomposition algorithm and a typical piecewise linearization method. Case studies have substantiated the higher computational efficiency of our IBD algorithm.

The application of power electronics can introduce flexibility into power systems, but it can also cause harmonic problems. As a typical application of power electronics, high-voltage direct current (HVDC) transmission exhibits very prominent harmonic problems. In recent years, many HVDC protection maloperation events caused by harmonic problems have occurred in the field. The phenomena of harmonic transfer and amplification in HVDC transmission have an undeniable impact on the overall operation of a power system. Previous studies of these phenomena have relied mainly on detailed electromagnetic transient simulations. However, these simulations normally suffer from a large computational burden and low efficiency due to the complexity of the models and the small simulation time steps used. Therefore, an efficient analysis model of harmonics for various HVDC operating modes is presented in this paper. This model is based on port theory; every part of the HVDC transmission system is represented by an equivalent two-port component to construct a unified equivalent circuit. The proposed model is simple to calculate, and only the connections of the two-port components need to be changed to model different operation modes. The proposed two-port model is applied for the harmonic analysis of an HVDC transmission system, and the validity and accuracy of the model are confirmed by detailed electromagnetic simulations. Moreover, the model can be easily applied to HVDC systems for harmonic transfer and amplification, and the effects of the HVDC transmission power and smoothing reactor on harmonic transfer and amplification are analyzed.

The increasing complexity of power systems, particularly the high renewable energy penetration, raises the necessity of incorporating detailed power system operation models into long-term planning studies. The classic short-term operation model, i.e., network-constrained unit commitment (NCUC), involves many binary variables and introduces computational challenges when applied to long-term planning optimizations. A high-efficiency and simplified NCUC model is required to incorporate operational flexibility in power system planning studies. This paper proposes a linearized NCUC formulation that has a high calculation performance and minor approximation errors compared to the full NCUC model. The proposed model combines the dispatch-only (DO) operation model and clustered unit commitment (CUC) model by introducing linking constraints between them such that the overall model guarantees both the transmission security constraints that are formulated in the DO model and the start-up/shut-down constraints of generating units that are formulated in the CUC model. A case study of a modified IEEE RTS-79 system is provided to demonstrate the validation and efficiency of the proposed simplified NCUC model as well as its effectiveness for power system planning studies.

Optimization of the stock management process is associated with the search for a forecasting model, a method for generation of a forecasting time series, a model of logistic operation, determining a reasonable level of reserve stocks and establishing the optimization criterion. Successful solution to the optimization problem in general can be achieved only if the whole complex of local management problems is successfully solved. In this case, the method of generation of a cointegrated time series of demand forecasting is the central element of the technology of optimal stocks management. This relates to the fact that probabilistic nature of demand is the main factor reducing efficiency of management in systems of this class. It was shown that the proposed method for improving management efficiency can be used in any economic system due to the possibility of construction of a single logistic operation model. The proposed approach is based on formation of a time series specifically designed to solve the problem of forecasting the demand in stocks buffering systems. Such a series contains both information on sales volumes and data related to consumer demand. Since consumer activity is ahead of the process of physical consumption of products, it becomes possible to use anticipatory markers in forecasting problems. The study of operational processes using a verified indicator of efficiency has confirmed the hypothesis of presence of anticipatory markers within the framework of the formed forecast time series. It has been established that the maximum management efficiency can be achieved in the case of a lower construction accuracy of the forecast model. This is due to the fact that the logistic operation model takes into account the costs of movement of products and their valuation at the operation input and output.

The rational allocation of chiller group plays an important role in the efficient operation of generating units. In this paper, the running characteristics of chiller group under full load are studied. Through the construction of the operation model of the chiller group, the relationship between the start and stop collocation and the operation characteristics of the unit group was explored, and the influence of the unit capacity configuration and the number of table number configuration on the running energy consumption characteristics was studied. The results showed that: the greater the difference of host capacity in the group, the lower the energy consumption under full load. In this example, the difference of energy consumption can reach 8%. At the same time, through the increase in the number of generating units, the difference in host capacity within the group had less impact on energy consumption at full load. For a unit group with the same host capacity, the more the number of units was configured, the lower the operating energy consumption. It provided theoretical support for the selection, collocation and operation management of chillers in practical projects.

Battery swapping stations (BSSs) have a potential of becoming a part of the electric vehicle charging infrastructure mix. A number of aggregated BSSs have a significant capacity and can act as strategic players in electricity market. This paper formulates a discrete cluster model for optimal operation of aggregated BSSs that take part in the day-ahead energy and reserve capacity markets and have sufficient combined capacity to affect market prices. In the presented bilevel structure the upper-level problem optimally defines charging and discharging schedules, as well as reserve capacity, of the aggregated BSSs, while the lower-level problem clears the market and provides locational marginal prices for each BSS, as well as the reserve capacity prices. This problem is converted into a single-level equivalent using the Karush-Kuhn-Tucker optimality conditions. The presented case study reveals that the BSSs can collect significant revenues when providing reserve capacity. The main benefit of the proposed cluster model is computational tractability and solution accuracy, which depends on the resolution of the clusters.

Connections among multi-energy systems become increasingly closer with the extensive application of various energy equipment such as gas-fired power plants and electricity-driven gas compressor. Therefore, the integrated energy system has attracted much attention. This paper establishes a gas-electricity joint operation model, proposes a system evaluation index based on the energy quality character after considering the grade difference of the energy loss of the subsystem, and finds an optimal scheduling method for integrated energy systems. Besides, according to the typical load characteristics of commercial and residential users, the optimal scheduling analysis is applied to the integrated energy system composed of an IEEE 39 nodes power system and a 10 nodes natural gas system. The results prove the feasibility and effectiveness of the proposed method.

A model for operating an energy hub-based multiple energy generation micro-grid is optimized using the demand response program. The optimized objective model is validated against energy demand of a residential building in Tehran, Iran. The mathematical model and optimal analysis of the proposed tri-generation micro-grid are implemented by using a real-world modelling and considering the constraints of the storage system, demand response program and the performance of the devices and the power and gas grids. The dynamic optimal operation model is prepared on the basis of the mixed integer linear programming on the subsequent day and is solved to minimize the costs of energy supply. To demonstrate the improvements, different scenarios are developed so that the renewable energy resources and storages are fed into the combined cool, heat and power system gradually. The results reveal that the inclusion of each element results in a significant improvement in the operational parameters of the micro energy grid. Scenario 1 includes a combined cool, heat and power system alone, Scenario 2 is supplemented with renewable wind and solar energy resources in addition to combined cool, heat and power system and Scenario 3 includes electrical, heat and cold storages in addition to combined cool, heat and power system and renewable energy sources. Scenario 4 is similar to Scenario 3 in terms of equipment, but the only difference lies in the use of the demand response program in the former. Total operational cost is 12.7% lower in Scenario 2 than in Scenario 1, 9.2% lower in Scenario 3 than in Scenario 2 and 8.6% lower in Scenario 4 than in Scenario 3. Practical application: An optimized operation method is prepared for combined cool, heat and power systems running in different operation modes in which renewable energy sources and storages are added to the combined cool, heat and power and the demand response program is applied. The results reveal that the cost of energy supply, including the cost of electricity, gas and pollutant emissions, is reduced and the qualitative parameters of the operation, including efficiency and reliability of building micro-grid, are increased. The proposed algorithm and the evaluation method will enable building operators to plan demand response activity on the residential building in Tehran, while this can be extended to other buildings too.

The net demand of distribution systems of renewable energy shows strong daily and seasonal patterns that may cause a loss of operation and constriction. Battery energy storage systems (BESS) allow peak load shaving and are used to enhance the reliability of distribution systems. In addition to the problem of an unbalanced net demand, there can be significant net demand fluctuation for different times and locations. To address this, mobile BESS (MBESS) can offer advantages over static BESS (SBESS) in operation flexibility, though may require higher engineering costs. In this study, an operation model was proposed to coordinate static and MBESS to improve overall system economic efficiency and reliability. On the basis of this model, a framework was proposed to optimally allocate MBESS/SBESS in a distribution system based on cost–benefit analysis. Using this approach, the optimal operation schedules for MBESS and SBESS can be simultaneously obtained. The proposed optimisation problem uses a new evolution algorithm, a natural aggregation algorithm. A case study on the IEEE test system successfully verified the effectiveness of the proposed approach for the optimal allocation of MBESS/SBESS in distribution systems.

For bulk AC/DC hybrid transmission systems with high wind power penetration, this paper presents a distributed dispatch approach to the security-constrained unit commitment (SCUC) problem. To fully use the flexible adjustment capability of the HVDC tie-line to promote inter-regional wind power accommodation, an operation model for the HVDC tie-line is presented. Then, a distributed SCUC approach based on the analytical target cascading technique is proposed, where the day-ahead SCUC problem is decomposed into an upper-level master problem and parallel sub-problems of lower-level regional dispatch. The master problem is in charge of determining the day-ahead transmission plan for the HVDC tie-line, and the lower-level dispatch centers independently solve their SCUC problems in parallel in accordance with the hierarchical and partitioned power scheduling mode. Simulations show that considering the flexible and controllable nature of the HVDC tie-line, the proposed distributed dispatch approach for bulk AC/DC hybrid systems has the advantages of promoting inter-regional wind power accommodation and improving the economics of the overall system operation while relieving the peak regulation pressure of the receiving-side system.

In this paper, a comprehensive two-stage robust distribution system operation model is proposed by adjusting the charging of electric vehicle aggregators (EVAs) and curtailing loads. Because uncertainties in EVA charging demands are involved in the second stage of the adopted two-stage framework, distributionally robust optimization is used to improve the average economic performance of the proposed model, and security of distribution system operation is guaranteed by applying the Farkas lemma and robust optimization. The proposed model is solved by iteratively adding optimality cuts and feasibility cuts through a novel constraint generation algorithm, whose mathematical Proof is provided. The case studies show that the proposed model is capable of properly handling EVA uncertainties and coordinating EVA charging and load curtailments. The optimal coordination depends on several key parameters including the cost coefficients of delaying EVA charging and curtailing loads, the limits on delaying EVA charging, the system load level, and the EVA uncertainty level.

Condensation occurring on metal cabinet surface seriously affects the maintenance and operation safety. In order to prevent condensation, desiccant coating surface method is proposed and investigated in this paper. Basic operation model of desiccant coated cabinet to realize anti-condensation and regeneration is first introduced, then an experimental setup is established to investigate and compare both anti-condensation and regeneration capacities of zinc sheet samples with and without desiccant coating. Widely adopted silica gel is adopted as desiccant material in this study. Results show that silica gel coated sample can effectively extend the anti-condensation time 2 to 6 times longer. When surface temperature is between 35 °C and 50 °C under experimental conditions, the coated desiccant material can be regenerated and recycled. The corresponding longest regeneration time is 160 min. The dynamic adsorption capacity of coated silica gel is also analyzed and it is found that both adsorption rate and mass of silica gel increases under lower surface temperature, and improvement index gradually becomes less obvious with decreasing surface temperature, the corresponding saturated adsorption mass of silica gel before condensation is about 0.3 g/g. Finally a practical case study of desiccant coated cabinet is conducted at Shanghai and operation testing shows that occurrence probability of condensation for desiccant coated cabinet greatly decreases from 70% to 10% compared with conventional cabinet in plum rain season, and there is no condensation occurring within desiccant coated cabinet in early autumn. The performance of silica gel coated cabinet is tested under relatively extreme condition with simulated high humid environment (30 °C, 90%) lasting for 7–10 h, it is found that the cabinet could continuously operate for about 60 h without condensation. In conclusion, desiccant coating method is proven to be an effective way to prevent condensation.

The intermittency of renewable energy poses challenges on the reliable and economical operations of microgrids. This paper considers a grid-connected microgrid model which consists of a logistics distribution system, where electric vehicles (EVs) depart from the depot, deliver the goods to multiple demand loads, and then, return to the depot. Based on this, this paper studies the coordinated dispatch strategies of EVs to smooth renewable energy and load fluctuations of the microgrid while ensuring the quality of logistics services. A microgrid operation model is proposed to optimize the driving routes, fast-charging time, and regular-charging/discharging strategies of EVs. Specifically, the objective is to minimize the overall operation cost of the microgrid while satisfying the requirements of the logistics distribution tasks. A self-adaptively imperialist competitive algorithm is proposed to solve the model. The simulation results demonstrate the effectiveness of the proposed model.

The thermal inertia of district heating system (DHS) has been considered in the combined heat and power (CHP) system operation to reduce wind curtailments. While DHS usually operates at the quality-regulation mode, under which the potential of public buildings to regulate heat demand is not fully exploited. In this paper, according to the different heat demand characteristics of public buildings (PB) and residential buildings (RB), a CHP system operation model adopting a centralized control with flow varied by steps in DHS operation regulation is proposed. Case studies show that the proposed model has better performance in economic benefits and wind power integration.

Nowadays, large-scale power outages induced by ever-growing natural disasters are increasing at a galloping rate. In this regard, this paper aims at improving the resilience of power distribution systems in facing high impact low probability (HILP) events in the planning phase of the system. To this end, optimal switch placement along with the optimal distribution line hardening strategies is proposed to mitigate the repercussions of the natural calamities. So, decreasing the failure probability of the distribution lines alongside the optimal placement of remote-controlled switches (RCSs) to increase the maneuvering capability of the system, the system operator would be able to strengthen the system and cope with disastrous events. To do so, in this paper, a proactive strategy is used to simulate the operation of the system in confronting the HILP events. The proposed model implemented on a real 47-bus power distribution system and the efficacy of the model is approved by the simulation results in enhancing the resilience level of the system.

Considering the time-space relationship between energy storage and flexible load together with the influence of network power flow, an optimal operation model of active distribution network is established, with the but of maximizing renewable energy utilization, minimizing network loss and maximizing user satisfaction. At the same time, the coordination among the units of active distribution network is quantified by setting the scheduling priorities of controllable distributed generation units, energy storage systems and flexible loads. Finally, an example is analyzed.

Large-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network. Young Elite Scientists Sponsorship Program of the Chinese Society of Electrical Engineering (CSEE-YESS-2018006); Scientific Foundation for the National Natural Science Foundation of China (51807127).

This study discusses the impact of market rules on the generation (and curtailment) from intermittent energy sources (IES), such as the design of support schemes, the priority dispatch rule for IES, negative prices and economic compensation for IES curtailment. It also describes the approaches applied in some European countries and alternative designs. An evaluation of these different market rules is assessed for the Spanish 2020 scenario by using a system operation model. The results show that with a feed-in-tariff and the priority dispatch rule, more generation from IES is fed-in into the system, and lower emissions are obtained. With negative prices and demand response, the feed-in-tariff scheme provides lower demand costs. With market remuneration and ex-post remuneration (as implemented in Spain in 2015), IES generation fed-in in the system is reduced and demand costs are increased. Furthermore, the curtailment of IES increases with higher penetration levels of IES. The increase in the curtailment is significantly higher for wind power as generation and demand are less correlated than solar generation and demand. The design of curtailment compensation and support schemes together become crucial to incentivise IES investments with higher value for the system.

To obtain operation benefits of both distribution network (DN) and microgrids (MGs), a multi-objective bi-level optimal operation model for DN with grid-connected MGs is explained. Starting from forecast of load and generation in MGs, upper-level (DN level) model determines the optimal dispatch of DN to achieve its power loss reduction and voltage profile improvement. Lower-level (MG level) model accepts the dispatch requirements from upper-level and determines the optimal operation strategy of distributed generators in MGs. Their energy utilization is increased with the consideration of wind curtailment, solar curtailment and other factors such as environmental benefits. With the mutual influence and constraints between upper-level and lower-level model, MGs could accommodate the optimal dispatch of DN. To solve the bi-level model, a combination method based on self-adaptive genetic algorithm and non-linear programming is put forward. IEEE 33 DN with Europe typical MGs and a real system are presented to perform several simulations, and results show the over-all optimal operation schemes of both DN and MGs compared with traditional dispatch approach, thereby validating the efficiency of the proposed model.

Pyroprocessing is a complicated batch-type operation, involving a highly complex material flow logic with a huge number of unit processes. Discrete event system modeling was used to create an integrated operation model for which simulation showed that dynamic material flow could be accomplished to provide considerable insight into the process operation. In the model simulation, the amount of material transported upstream and downstream in the process satisfies a mass balance equation while considering the hold-up incurred by every batch operation. This study also simulated, in detail, an oxide reduction group process embracing electrolytic reduction, cathode processing, and salt purification. Based on the default operation scenario, it showed that complex material flows could be precisely simulated in terms of the mass balance. Specifically, the amount of high-heat elements remaining in the molten salt bath is analyzed to evaluate the operation scenario.

This paper develops a novel continuous-time look-ahead optimization model for co-optimizing balancing energy and flexible ramp products in real-time power systems operation. In addition, a continuous-time day-ahead operation model is developed to commit and schedule an adequate subset of generating units for providing balancing energy and ramping requirements in real-time operation. The up and down flexible ramp products are defined as continuous-time decision trajectories that continuously supply the real-time up and down ramping requirements of the system. The deliverability of flexible ramp products is secured through reserving the respective power requirements in generation capacity constraints of generating units. A function space solution method is proposed to reduce the dimensionality of day-ahead and real-time models through projecting them into finite-dimensional function spaces spanned by Bernstein polynomials, where the real-time operation decisions are modeled with a higher accuracy level than day-ahead decisions. Simulations are conducted on the IEEE-RTS system using CAISO load data, and the numerical results showcase the effectiveness of the proposed model in supplying the energy and flexible ramp requirements and avoiding ramping scarcity in the system, while reducing the operation costs.

Nuclear energy and renewable energy have been deployed and expanded globally to reduce greenhouse gas emissions and improve the environment. The demand for flexibility is increasing sharply with the rapid penetration of intermittent renewables in the power system. Meanwhile, in the coal dominated power system, the reduction on the share of coal-fired power will leave the power system with a more serious shortage of flexibility. The flexible nuclear operation has aroused wide attention as an important flexibility resource of the power system. To evaluate the role of the flexible nuclear operation on the coal-dominated power system, we first model the flexible operation of nuclear power plants (NPPs). Then we integrate the operation model of NPPs in a unit commitment and economic dispatch model. The model is further integrated into the power system operation simulation framework. Finally, we analyze the potential benefits of flexible nuclear operation using the power system operation simulation tool for a coal-dominated power system: Shandong province of China. The simulation results indicate that the flexible nuclear operation could effectively reduce start-up and shut-down times of coal-fired power units and help the power system to achieve a higher renewable energy penetration of 47.98%. The research suggested that it is feasible and beneficial to use NPPs to produce flexibility in the future power system.

An increasing number of microgrids (MGs) with high penetration of renewables are being integrated into the distribution system (DS). Multiple MGs as stakeholders in the future retail markets may sign a cooperation contract when faced with high uncertainties of renewable energies and complementary features among MGs. The optimal coordinated operation mechanism for the MGs would be essential. In this paper, we propose a decentralized optimal operation framework to coordinate the power exchange among MGs abiding by a cooperative contract based on the analytical target cascading (ATC) algorithm. Furthermore, to address the renewable energy uncertainties in the operation strategy of each MG, a two-stage adjustable robust optimization model is devised to coordinate the power exchange between the DS and MGs, the demand response, the output of controlled distributed generations (DGs), and the charging/discharging behavior of energy storage system (ESS). Then, the column and constraint generation method (CCG) is applied to solve the proposed robust optimization model. Comparative studies based on a DS with three typical MGs are performed to validate the effectiveness of the proposed methods.

The short-term optimal operation model discussed in this paper uses the 2016 to 2018 daily and monthly data of Baluchaung II hydropower station to optimize power generation by minimizing water consumption effectively in order to get more revenue from optimal operation. In the first stage, run-off-river type Baluchaung II hydropower station data was applied in a mathematical model of equal micro-increment rate method for optimal hydropower generation flow distribution unit results. In the second stage, dynamic programming was used to get optimal hydropower generation unit distribution results. The resultant data indicated that optimized results can effectively guide the actual operation run of this power station. The purpose of the optimal load dispatching unit was to consider the optimal power of each unit for financial profit and numerical programming on the actual data of Baluchaung II hydropower plant to confirm that our methods are able to find good optimal solutions which satisfy the objective values of 17.75% in flow distribution units and 24.16% in load distribution units.

The demand response and battery energy storage system (BESS) will play a key role in the future of low carbon networks, coupled with new developments of battery technology driven mainly by the integration of renewable energy sources. However, studies that investigate the impacts of BESS and its demand response on the adequacy of a power supply are lacking. Thus, a need exists to address this important gap. Hence, this paper investigates the adequacy of a generating system that is highly integrated with wind power in meeting load demand. In adequacy studies, the impacts of demand response and battery energy storage system are considered. The demand response program is applied using the peak clipping and valley filling techniques at various percentages of the peak load. Three practical strategies of the BESS operation model are described in this paper, and all their impacts on the adequacy of the generating system are evaluated. The reliability impacts of various wind penetration levels on the generating system are also explored. Finally, different charging and discharging rates and capacities of the BESS are considered when evaluating their impacts on the adequacy of the generating system.

How to make operation strategy for regional multi-energy system with less energy loss and small cost is worth studying. Optimal operation model of regional multi-energy system is built based on heating water network model, considering heating loss of hot water pipes caused by temperature drop and power consumption of circulating water pump caused by water flow. The corresponding algorithm is introduced to achieve the solution of the model built in this paper. Finally, a supposed multi-energy system is used to conduct a simulation, and the results show that the proposed optimization model of making operation strategy for regional multi-energy system can further reduce energy loss and operation cost.

In this paper, we present an optimal operation model for an active distribution utility with inflexible and flexible loads. The proposed model addresses how the flexible loads should be operated to minimize the utility operation cost as well as maintaining the distribution network safe. Moreover, we analyze how the flexible loads can provide the reserve service for the electricity market with highly renewable resources penetration. Assuming T time intervals, we determine the optimal energy and reserve bids to the electricity market that minimize energy procurement cost of the utility and fully comply with the operational standards of the distribution network. From a set of numerical experiments on the 33-bus system, we present the advantages of our proposed model.

With the increasing penetration of renewable energy sources (RES) and strong coupling between electric power systems and other forms of energy, there are vast opportunities and challenges for every practitioner in energy field, especially so many stochastic characteristics to suppress, such as wind, photovoltaic and load demand. This manuscript establishes an integrated optimal operation model of energy internet considering the multi-time-scale stochastic characteristics of RES. Finally, the particle swarm optimization (PSO) algorithm is applied to the optimization of the proposed model and the simulation results show that the proposed model is correct and effective.

In order to satisfy the practical requirement of the power grid in China, this paper presents a multiobjective operation model for a cascaded hydropower system simultaneously considering th...

This paper presents studies on improving the robustness of shipboard dc hybrid power systems. The shipboard power system can be regarded as an islanded microgrid. The objective, in particular, is to reduce the load curtailment when generator failures take place in the islanded microgrid. By applying the basic unit commitment (UC) operation model, the loads can be fully supplied during normal condition. However, a portion of the loads has to be shed if any generator fails. Two approaches are proposed to mitigate the loss of load during and after fault. They are developed by modifying the UC models. These approaches are evaluated for reducing load curtailment under loss of generation. Selected case studies indicate encouraging results that the loss of load can be reduced during and after generator failure. For carrying out the case studies, the optimization model is built on General Algebraic Modeling System (GAMS) platform.

This article investigates an optimal operation model which is based on improved genetic algorithm for natural gas pipeline network. First, the maximum benefit and the maximum flow were chosen as the objective function, and several conditions were selected as the constraints including the input and output of gas, the input and output pressure of gas, the handling capacity of compressive station, the strength of the pipeline, decreasing of the pipeline pressure, the compressor, the valve, and the flow balance of pipe network node. On the basis of the above two aspects, the optimal mathematical operation model of natural gas pipeline network is established. Second, an improved genetic algorithm is proposed due to the possibility that the fitness value of particular individual in the initial population is abnormal and the possibility that the probabilities of the crossover and the mutation are too high or too low. Finally, a medium-pressure pipe network is taken as a study example. Compared with the basic genetic algorithm and the non-optimized genetic algorithm, the maximum benefit and maximum flow rate of the improved genetic algorithm are increased by 3.09%, 1.61%, 5.98%, and 2.44%, respectively.

Photovoltaic- (PV-) based battery swapping stations (BSSs) utilize a typical integration of consumable renewable resources to supply power for electric vehicles (EVs). The charging strategy of PV-based BSSs directly influences the availability, cost, and carbon emissions of the swapping service. This paper proposes an optimal charging strategy to improve the self-consumption of PV-generated power and service availability while considering forecast errors. First, we introduce the typical structure and operation model of PV-based BSSs. Second, three indexes are presented to evaluate operational performance. Then, a particle swarm optimization (PSO) algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. The proposed charging strategy helps decrease the impact of forecast uncertainties on the availability of the battery swapping service. Finally, a day-ahead operation schedule, a real-time decision-making strategy, and the proposed PSO charging strategy for PV-based BSSs are simulated in a case study. The simulation results show that the proposed strategy can effectively improve the self-consumption of PV-generated power and reduce charging cost.

This paper explores from a techno-economic perspective the value of increasing distribution network capacity via Demand Side Response (DSR) services provided by smart multi-energy systems enabled by an intelligent information platform (i.e., the District Information Modelling and Management for Energy Reduction (DIMMER) platform under development in the homonymous European project). More specifically, the potential of smart multi-energy systems comprising gas boilers, Electric Heat Pumps (EHP) Combined Heat and Power (CHP) and a smart information platform to provide distribution network capacity is explored. For this purpose, a multi-energy district operation optimization approach coupled with a distribution network planning framework is proposed. The district operation model identifies the behavior of end-users who may aim at minimizing their energy costs and/or trade capacity with DNOs. The network planning framework calculates the value for DNOs from additional network capacity provided by customers. The methodology is illustrated via a real UK multi-energy district at the University of Manchester and the relevant electricity, heat and gas networks. The results highlight that the flexibility inherent in multi-energy systems can provide significant economic benefits for both end-users and DNOs.

In this paper, a mixed-integer linear programing (MIL?) model for the traffic behavior of plug-in electric vehicles (PEVs) in a multi energy system (MES) is proposed. It is assumed that two micro-MESs are covering two traffic zones with different consumption patterns. The difference between these two micro-MESs is not only the different multi energy demand (MED) they provide, but also different PEV traffic pattern that travel in these two micro-MESs. The PEVs traffic pattern and their behavior in using parking lot (PL) or charging station (CS) as their charging places is integrated in the MES operation model. The results demonstrate an improved strategy of the MES operator in using its components, such as combined heat and power (CHP) unit and auxiliary boiler (AB), in response to extra added load of the PEVs. The stochastic behavior of the PEVs is implemented in the model through various scenarios of arrival and departure.

The ability of the grid-friendly microgrid responding the system operation demand is analyzed by exploiting potentialities of grid operation from the traditional load side. The volatility of DGs output is balanced by including controlling EVs the charging and discharging. The output of microgrid is scalable through coordinating all kinds of DGs based on which a microgrid economic operation model is built. The simulation result shows the microgrid can absorb internal intermittent power and achieve system dispatching well by coordinating kinds of powers. Its dispatching potentiality is sizable.

The economic operation of battery swapping stations (BSSs) is significant for the promotion of large-scale electric vehicles. This paper develops a linear programming model to maximize the daily operation profits of a BSS by considering constraints of the battery swapping demand of users and the charging/discharging balance of batteries in the BSS. Based on the BSS configuration and data from electric taxis in Beijing, we simulate the operation situation and charging/discharging load of the BSS in nine scenarios with two ordered charging and discharging strategies. The simulation results demonstrate that the model can achieve the maximum daily profits of the BSS. According to the sensitivity analysis, the battery swapping price for batteries is the most sensitive, followed by the number of batteries in the BSS, while the operation-maintenance costs and battery depreciation costs are least sensitive. In addition, the charging and discharging of batteries in the BSS can be coordinated by increasing the battery quantity of the BSS and formulating the ladder-type battery swapping price.