Optimal Operation and Size for an Energy Hub with CCHP

The interest in distributed generation has been increasing in recent years, especially due to technical devel- opment on generation systems that meet environmental and energy policy concerns. One of the most impor- tant distributed energy technologies is Combined Cooling, Heat and Power (CCHP) systems. CCHP is a small and self-contained electric, heating and cooling generation plant that can provide power for households, commercial or industrial facilities. It can reduce power loss and enhance service reliability in distribution systems. The proposed method in this paper determines the optimal size and operation of CCHP, auxiliary boiler and also heat storage unit as elements of an energy hub, for users by an integrated view of electricity and natural gas network. Authors apply cost and benefit analysis in the optimization. To confirm the proposed method, the optimum sizes of these elements are determined for a hotel in Tehran as a case study.

Optimal Operation Strategies for a Large Multi-Chiller System Based on Cooling Load Forecast

In semiconductor and electronics factories, large multi-chiller systems are needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, an optimal flexible operation scheme is developed based on a two-dimensional time-series model to forecast the cooling load of multi-chiller systems with chiller units of different cooling capacities running in parallel. The optimal integrity scheme can be obtained using the Mixed Integer Nonlinear Programming method, which minimizes the energy consumption of the system within a future time period. In order to better adapt the change of cooling load, the operation strategy of regulating the chilled water flowrates is employed. The chilled water flowrates are set as a design variable. When the chillers are running, their chilled water flowrates can vary within limits, whereas the flowrates are zero when the chillers are unloaded. This forecasting method provides integral optimization within a future time period and offers the operating reference for operators. The power and advantages of the proposed method are presented using an industrial case to help readers delve into this matter.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

Optimal operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid. © 2014 Chinese Association of Automation.

Optimal scheduling of a township integrated-energy system using the adjustable heat-electricity ratio model

With the expansion and implementation of rural revitalization strategies,there is a constant need for new energy sources for the construction of new townships.Consequently,integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly.Biomass energy,a renewable green energy source with low pollution and wide distribution,has significant application potential in integrated energy systems.Considering the application of biomass energy in townships,this study established an integrated biomass energy system and proposed a model to optimize its operation.Lowest economic cost and highest clean energy utilization rate were considered as the objective functions.In addition,a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass.Finally,a simulation analysis conducted for a town in China was discussed,demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate.Moreover,by adjusting the heat-electricity ratio,the economic cost was further reduced by 6.70%,whereas the clean energy utilization rate was increased by 5.14%.

Research on Storage Capacity of Compressed Air Pumped Hydro Energy Storage Equipment

Compressed air pumped hydro energy storage equipment combines compressed air energy storage technology and pumped storage technology. The water is pumped to a vessel to compress air for energy storage, and the compressed air expanses pushing water to drive the hydro turbine for power generation. The novel storage equipment saves natural gas resources, reduces carbon emission, and improves the controllability and reliability. The principle of compressed air pumped hydro energy storage is introduced and its mathematical model is built. The storage and generation process of the novel equipment is analyzed using the model. The calculation formula of the storage power is deduced in theory in different situations of isothermal and adiabatic compression. The optimal storage scheme is given when the capacity and withstand pressure of the vessel is definitive, and the max available capacity and the equipment utilization efficiency evaluation of the scheme is given.

Multi-user Motion JPEG2000 over wireless LAN: run-time performance-energy optimization with application-aware cross-layer scheduling

This paper introduces a video application-aware cross-layer framework for joint performance-energy optimization,considering the scenario of multiple users upstreaming real-time Motion JPEG2000 video streams to the access point of a WiFi wireless local area network and extends the PHY-MAC run-time cross-layer scheduling strategy that we introduced in (Mangharam et al., 2005; Pollin et al., 2005) to also consider congested network situations where video packets have to be dropped. We show that an optimal solution at PHY-MAC level can be highly suboptimal at application level, and then show that making the cross-layer framework application-aware through a prioritized dropping policy capitalizing on the inherent scalability of Motion JPEG2000 video streams leads to drastic average video quality improvements and inter-user quality variation reductions of as much as 10 dB PSNR, without affecting the overall energy consumption requirements.

Telecommunications and Energy Infrastructure Sharing: Technical and Socio-Economic Impact Analysis in a Multi-Operator Environment in Burundi

The sharing of telecommunications infrastructure and power supply equipment is currently an applicable and very common model for grouping signal transmission and reception equipment and their power supply on the same site to ensure coverage of fixed, mobile, Internet and radio and television broadcasting networks. This study consists of producing an inventory of telecommunications and energy infrastructure sharing, focusing on the one hand on analyzing the impacts of active and passive sharing of telecommunications infrastructure from a technical point of view, particularly in terms of legal framework, deployment, coverage and exposure to electromagnetic radiation, and on the other hand on identifying the effects of infrastructure sharing from a socio-economic point of view in a multi-operator mobile telephony environment, by indicating the economic value of the revenue generated as a result of infrastructure sharing. Finally, the results will contribute to identify strategies for ensuring maximum deployment and coverage of the country, and for developing the information and communication technologies (ICT) sector in order to contribute to the digital transformation by digitising services using mobile telephony and the Internet in Burundi.

Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet

Unlike conventional power systems, the upcoming energy internet U+0028 EI U+0029 emphasizes comprehensive utilization of energy in the whole power system by coordinating multi-microgrids, which also brings new challenge for the energy management. To address this issue, this paper proposes a novel consensus-based distributed approach based on multi-agent framework to solve the energy management problem of the energy internet, which only requires local information exchange among neighboring agents. Correspondingly, two consensus algorithms are presented, one of which drives the incremental cost of each distributed generator U+0028 DG U+0029 converge to the state of the leader agent-energy router, and the other one is used to estimate the global power mismatch, which is a first-order average consensus algorithm modified by a correction term. In addition, in order to meet the supply-demand balance, an effective control strategy for the energy router is proposed to accurately calculate the power exchange between the microgrid and the main grid. Finally, simulation results within a 7-bus test system are provided to illustrate the effectiveness of the proposed approach. © 2014 Chinese Association of Automation.

Optimization on Water Resource System Operation Policy during Drought

The mixed linear programming model is commonly recognized to be an effective means for searching optimal reservoir operation policy in water resources system. In this paper a multi-objective mixed integer linear programming model is set up to obtain the optimal operation policy of multi-reservoir water supply system during drought, which is able to consider the operation rule of reservoir-group system within longer-term successive drought periods, according to the basic connotation of indexes expressing the water-supply risk of reservoir during drought, that is, reliability, resilience and vulnerability of reservoir water supply, and mathematical programming principles. The model-solving procedures, particularly, the decomposition-adjustment algorithm, are proposed based on characteristics of the model structure. The principle of model-solving technique is to decompose the complex system into several smaller sub-systems on which some ease-solving mathematical models may be established. The objective of this optimization model aims at maximizing the reliability of water supply and minimizing the maximum water-shortage of single time-period within water- supply system during drought. The multi-objective mixed integer linear programming model and proposed solving procedures are applied to a case study of reservoir-group water-supply system in Huanghe-Huaihe River Basin, China. The desired water-shortage distribution within the system operation term and the maximum shortage of single time-period are achieved. The results of case study verifies that the lighter water-shortage distributed evenly among several time-periods can avoid the calamities resulted from severe water shortage concentrated on a few time-periods during drought.

A multi-objective train-scheduling optimization model considering locomotive assignment and segment emission constraints for energy saving

Energy saving and emission reduction for railway systems should not only be studied from a technical perspective but should also be focused on management and economics. On the basis of relevant trainscheduling models for train operation management, in this paper we introduce an extended multi-objective trainscheduling optimization model considering locomotive assignment and segment emission constraints for energy saving. The objective of setting up this model is to reduce the energy and emission cost as well as total passenger- time. The decision variables include continuous variables such as train arrival and departure time, and binary vari- ables such as locomotive assignment and segment occu- pancy. The constraints are concerned with train movement, trip time, headway, and segment emission, etc. To obtain a non-dominated satisfactory solution on these objectives, a fuzzy multi-objective optimization algorithm is employed to solve the model. Finally, a numerical example is performed and used to compare the proposed model with the existing model. The results show that the proposed model can reduce the energy consumption, meet exhausts emission demands effectively by optimal locomotive assignment, and its solution methodology is effective.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difficult areas of study.As every sensor node has afinite amount of energy.Battery power is the most significant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modification,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efficient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sfindings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

Multi Objective Multireservoir Optimization in Fuzzy Environment for River Sub Basin Development and Management

In this paper, a multi objective, multireservoir operation model is proposed using Genetic algorithm (GA) under fuzzy environment. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFU-OPT) model for the present study is developed in ‘C’ Language. The GA parameters i.e. population size, number of generations, crossover probability, and mutation probability are decided based on optimized val-ues of fitness function. The GA operators adopted are stochastic remainder selection, one point crossover and binary mutation. Initially the model is run for maximization of irrigation releases. Then the model is run for maximization of hydropower production. These objectives are fuzzified by assuming a linear membership function. These fuzzified objectives are simultaneously maximized by defining level of satisfaction (?) and then maximizing it. This approach is applied to a multireservoir system in Godavari river sub basin in Ma-harashtra State, India. Problem is formulated with 4 reservoirs and a barrage. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. This optimal operation policy so deter-mined is compared with the actual average operation policy for Jayakwadi Stage-I reservoir.

Multi-objective optimization scheduling for new energy power system considering energy storage participation

For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.

Optimization operation model of electricity market considering renewable energy accommodation and flexibility requirement

The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumption and improve power system flexibility,a bi-level optimal operation model of the electricity market is proposed.A probabilistic model of the flexibility requirement is established,considering the correlation between wind power,photovoltaic power,and load.A bi-level optimization model is established for the multi-markets;the upper and lower models represent the intra-provincial market and inter-provincial market models,respectively.To efficiently solve the model,it is transformed into a mixed-integer linear programming model using the Karush–Kuhn–Tucker condition and Lagrangian duality theory.The economy and flexibility of the model are verified using a provincial power grid as an example.

Planning and design of a micro energy network for seawater desalination and regional energy interconnection

With the rapid developme nt of the economy, the continu ously in creasing populati on, and ongoing climate change, the shortage of freshwater resources has become an increasingly important global problem. Seawater desalination tech no logy can effectively alleviate the pressure on freshwater supplies and has bee n in vestigated in many countries. However, the majority of existing projects focus on the research and development of desalination equipment and the use of new tech no logies and pay less atte ntion to the operation optimizati on of the desalinati on process. The micro energy n etwork (MEN) designed in this study is an efficient distributed energy supply system that can be used to simultaneously supply electricity, cooling, heating, and freshwater as photovoltaic power, wind power, combined heat and power (CHP), electric cooling and heating, and a seawater desalinati on device are in teg rated into the MEN. In this study, a model for operati on optimization of a MEN for seawater desalination was developed and the influences of the electric cooling and heating ratios and the operation optimization of the seawater desalination device were studied with the aim of minimizing the life cycle cost. Based on the results of this study, MENs can reduce the operation cost of desalination devices and improve the efficiency of renewable energy sources.