GT2017-63854

MODELING AND OPTIMAL OPERATION OF A NETWORK OF ENERGY HUBS SYSTEM WITH DISTRIBUTED ENERGY RESOURCES Shixi Ma Gas Turbine Research Institute Shanghai Jiao Tong University Shanghai P R China Dengji Zhou Gas Turbine Research Institute Shanghai Jiao Tong University Shanghai P R China Huisheng Zhang Gas Turbine Research Institute Shanghai Jiao Tong University Shanghai P R China Zhenhua Lu Gas Turbine Research Institute Shanghai Jiao Tong University Shanghai P R China ABSTRACT Energy hubs is a functional unit which is capable of transporting transforming and storing of several kinds of energy Several hubs can be combined as a network and achieve higher efficiency by exchanging energy with each other A framework to assist the decision making process towards the optimal integration of independent small scale distributed energy systems and traditional large scale CHP power plants is presented using an energy supply system in Shanghai as a case study A model of this complex network of energy hubs with renewable energy resources is presented based on energy flow between its constituent elements Furthermore GA optimization method is presented for short term 24 hour optimal operation Case study are undertaken on a 7 node energy system which comprises 4 energy hubs and 3 load hubs Results validate the high efficiency of this system Two cases with and without internal combustion engine failure within the network are considered The results showed that the proposed system can enhance the energy utilization efficiency and reduce the system operation cost even under a system contingency Keywords Energy hub Renewable energy Operational optimization Modeling NOMENCLATURE CCHP Combined Cooling Heating and Power CHP Combined Heating and Power GT Gas Turbine ICE Internal Combustion Engine PV Photovoltaic WT Wind Turbine HE Heat Exchanger AC Absorption Cooling COP Coefficient of Performance GA Genetic Algorithm DES Distributed Energy System MPC Model Predicted Control Energy carriers Input power of energy conversion hub Output power of energy conversion hub c Coupling between and Input power matrix of energy hub Output power matrix of energy hub Converter coupling matrix Input power of energy storage hub Input power of energy storage hub Efficiency of power storage Charge efficiency Discharge efficiency Energy stored in the device A period of time Charge or discharge speed m n Energy network nodes Total power injected at node m Efficiency of the transit between hubs Energy transfer from node m to n Input energy costs of the energy system Energy prices vector Energy consumption of the system decision various TGC Total gas consumption TEC Total electricity received from grid Proceedings of ASME Turbo Expo 2017 Turbomachinery Technical Conference and Exposition GT2017 June 26 30 2017 Charlotte NC USA GT2017 63854 1Copyright 2017 ASME INTRODUCTION In the past common energy infrastructures such as electricity natural gas and heat systems were operated independently 1 Promoted by several reasons such as more different types of distributed energy resources were added and more efficient energy use is needed due to environmental concern a number of recent publications suggest an integrated view of energy systems including multiple energy carriers instead of focusing on a single energy carrier 2 3 A new approach to overcome the problem is Energy hubs 4 Energy hubs that incorporate a variety of energy generation and energy transformation technologies can be used to cooperate different energy systems and allow massive energy storage by translating one type of energy into another When these hubs are combined as a network and allowed to exchange energy they create a number of potential advantages such as increased reliability load flexibility and optimization potential Related work review The energy hub concept was first introduced by Anderson in 2007 5 Energy hubs as introduced by 5 are interfaces between energy producer transportation infrastructure and consumers From a system point of view an energy hub should provide one of these basic features 1 in and output 2 conversion 3 storage of multiple energy carriers 6 Fig 1 shows an example of energy hub Input energy including electricity nature gas and heat are demanded from the corresponding infrastructure Energy is transported transformed and stored in the hub Electricity is transported with or without voltage change Natural gas can be used to generate electricity and heat e g CHP Cooling energy demand can be satisfied by using district heat e g absorption chiller Heat and electricity can also be stored in the hub for peak shaving Fig 1 Paradigm of energy hub Diverse studies have been accomplished since the concept was put forward Modelling design and operation of energy hubs are carried out in different scales These articles can roughly be divided into two types one node and multi node For the one node energy hub system it is much similar to distributed energy system DES Mohammad 7 considered a resident as an energy hub which equipped with CHP and plug in hybrid vehicle Modelling and operation optimization are then introduced Numerical results show that this system lead to lower customer payment cost Isha Sharma 8 proposed an operation framework using model predicted control MPC for optimal dispatch of various energy resources to meet the demand of a residential building The simulation results showed that using the proposed method in a building was possible to reduce the energy bill by optimally managing all the resources Moghaddam 9 presented a method for modeling energy hubs between its constituent elements The energy storage elements are not only used at the output of the hub but also are used as inputs for other elements inside the energy hub The numerical results by meeting loads of the building showed that it has a high efficiency As for the Multi node network energy flow between network nodes should be fully considered La Scala 10 have evaluated optimal energy flow in multicarrier network on 30 bus IEEE test network they have presented a multi objective model for reliable operation of energy system Also they have analyzed the effect of using interconnected energy hubs in power network Kristina Orehounig 11 offered energy system analysis for a village in Switzerland modeled as an energy hub which equipped with CHP and PV panel Numerical result show that the system lead to lower customer payment cost Alejandro 12 optimized the daily energy consumption in some neighbor buildings using the energy hubs concept The energy hubs concept in this paper includes district heating biomass and small hydrogen power plant They have surveyed the effect of using the energy hubs in energy economic and environmental cases Evins R 13 have presented a precise mixed integer programming model of energy hubs for scheduling of weekly generation of residential energy hubs limiting the number of start shutdown of equipment and considering partial load efficiency and increase the accuracy of energy loss calculation of energy storage facilities are the important innovative in this paper Shabanpour 14 have solved the 24h decision making problem using a well known modified teaching learning based optimization algorithm The energy hubs considered in this paper have three inputs of electricity gas and heating and two outputs Azadeh Maroufmashat 15 demonstrated that a network of energy hubs is an effective strategy for reducing system costs and emissions The case study showed that a network of two energy hubs there is no significant economic or emissions benefit A larger number of hubs is necessary in order to achieve significant benefits Main contribution of this paper The main aim of this paper is to develop the model and operation optimization of a district energy system based on energy hubs concept The system fully considers the integration of the traditional infrastructure electricity and heat network and the distributed energy systems By using optimization method the renewable energy resources and the distributed energy system can cooperate with the traditional power plant CHP in one network Peak shaving and higher efficiency may be achieved The highlights of this work are as follows 1 A model of energy network is built using the concept of energy hubs 2 Operation optimization is achieved using GA method in order to have a lower operation cost 3 Shanghai s energy networks and energy nodes in operation including independent small scale distributed energy 2Copyright 2017 ASME systems and traditional large scale CHP power plant are used in this paper which makes the optimization feasible 4 Two cases are studied to demonstrate the advantage of the proposed system 5 The possible coordination relationship between the traditional central energy node and the distributed energy node is simulated Considering the trade of energy development from centralized large scale to distributed small scale energy resources this paper can be used as reference of a possible control method Structure of this paper This paper focus on the modelling and operation optimization of an energy hubs system comprising traditional power plant renewable energy resources and distributed energy system The Modelling of the system is first described in Section 2 Including the detailed model process of transforming units storage and network in the system Optimization problem is constructed in Section 3 GA method is deployed to solve this problem The operation strategy is realized by considering the performance of different components while guarantee the consumer demand The advantages of proposed method and the results are analyzed in part 4 Finally the concluding points are drawn in Section 5 SYSTEM MODEL As the energy hubs have three features modelling of an energy hub can be divided into three parts conversion storage and network Energy conversion Considering a converter device that converts an input energy carrier into the relationship between input and output power in steady state can be expressed as follows 6 1 where and are the input and output powers defines the coupling between input and output power flow We consider a unit where multiple input energies are converted into multiple output This conversion can be achieved by a single device or by a combination of several converters The power inputs and power outputs can be expressed as 2 Where C is the converter coupling matrix it describes the mapping of the input powers to the output it can be either constant represent linear transformation or various yields nonlinear relations such as f Noted that matrix C is generally not invertible represents a possibility for optimization If C is regular then there is only one solution P for the demand L Consequently the power energy carriers P are vector of the decision variables of a optimization problem Energy storage The storage devices are considered to have two parts interface and internal storage Through the interface the power may be converted into another energy carrier and then be stored such as compressor air storage system however although the internal storage energy carrier is not equal to the input energy carrier it will final change to Therefore in the follow consideration power output and storage content are considered to be the same form 6 The input and output power values can be expressed as 3 Where is the efficiency of power storage Its value depends on the direction of power flow 1 4 Where and are the charging and discharging efficiencies The energy stored in the device after a certain period of time T can be expressed as follows 0 0 5 The corresponds to the time derivative of the stored energy Network In the energy network system hubs are connected by distribution networks the input energy flows to each hub come from the outside infrastructure such as utility grid or natural gas as well as renewable energy resources and the other hubs within the network The output of each hub is divided into two parts energy to supply the demand within the hub and the rest is sent to other hubs For node m in the network based on conservation laws the sum of all branch flows must be equal to the power injection 6 Where n is the set of nodes which connected to node m represent the loss during the transit between hubs is the total power injected at m For a system with N nodes N modal equations are needed to describe this network The losses coefficient can be derived as functions of the corresponding flows For example the losses on electricity line can be modeled as quadratic functions of the transmitted power heat line can be seen as a linear function of the water flow OPTIMIZATION PROBLEM Here the cost of the energy hubs system including gas and electricity ejected from outside is to be minimized in order to achieve a less operation cost min Pr 0 0 7 3Copyright 2017 ASME Where 1 is the energy prices vector is input energy costs We have the following limitations on converter storage and network 16 1 converter limits min 2 storage capacity limits min max 3 storage interface limits min 4 network flow limits min Solution algorithm The decision variables in this system are expressed as 8 and the optimization goal is to minimize function 7 In this paper considering that the efficiency of CHP may decrease significantly when the load is in low level Matrix C is considered to be non linear The constraint including equality and inequality are linear Based on the describe above it can be seen that the optimal operation for this system is formulated as a non convex nonlinear optimization problem with multi decision variables In this paper the optimization model is built in MATLAB and solved by GA Genetic Algorithm global solver which can deal with nonlinear optimization problem with linear constraint 14 The main parameters of the algorithm are summarized in Table 1 Table 1 Main parameters of genetic algorithm Parameters Value Population type Double vector Population size 200 Crossover fraction 0 4 Mutation function Gaussian Max generation 2000 CASE STUDY This system is a simulation of the energy supply system in Shanghai Case study are undertaken on a 7 node energy system which comprises 4 energy hubs and 3 load hubs The system aim to coordinate the traditional power plant hub 1 two distributed energy system hub 2 3 and centralized renewable energy hub 4 as showed in Fig 2 Position of system nodes and energy buses can be seen in Fig 3 Energy exchange between energy hubs is based on the structure of energy buses including electricity bus natural gas bus and heating bus Energy hub 1 is a traditional CHP power plant energy hub 2 is a CCHP node designed to serve commercial energy load energy hub 3 is also a CCHP node but without heat storage unit mainly used to satisfy the demand of residents Hub 4 is a renewable energy resource with multiple wind turbines and Photovoltaic panels ICE ICE Boiler Boiler Absorption chiller Absorption chiller Hub 2 Distributed energy system for commercial load GT GT Boiler Boiler Hub 3 Distributed energy system for residential load Compression refrigeration Compression refrigeration GT GT Hub 1 Traditional power plant PV PV Hub 4 Centralized renewable energy resources Heat exchange Heat exchange Heat exchange Heat exchange Heat exchange Heat exchange Wind Turbine Wind Turbine Fig 2 Schematic view of energy hubs 4Copyright 2017 ASME Hub1 Hub2 Hub3 Hub4 Commercial load Industrial load Residential load Heating network Natural gas network Electric network Fig 3 Schematic of the test system electric district heating and natural gas network Parameters for the four hubs are given in Table 2 The electricity and natural gas price are set to constant according the local commercial price in Shanghai China The data of solar radiation and wind speed on typical day is obtained from the software Homer Hybrid Optimization Model of Electric Renewable 17 Homer s micro power optimization software used in evaluating designs of both off grid and grid connected power systems for a variety of applications 18 The weather data used in the software are downloaded from National Oceanographic Data Center NCDC With the meteorological data as shown in Fig 4 the outputs of wind turbine and PV panels are calculated according to 17 Electricity heating and cooling loads for industrial commercial and residential areas on day are given in Fig 5 to 7 Table 2 Parameters for hubs in the system Hub 1 MW MW 35 22 15 30 Hub 2 MW MW 41 18 2 6 MW MW COP heatTank m3 5 6 5 1 2 25000 Hub 3 MW MW 33 25 2 5 5 Hub 4 MW MW 2 5 15 Table 3 Electricity price and gas price kW h m Price 0 0945 0 3926 Fig 4 Solar radiation and wind speed on typical day Fig 5 Cooling heating and electricity loads for commercial areas on a typical day Fig 6 Cooling heating and electricity loads for industrial areas on a typical day 0510152025 0 0 0 2 0 4 0 6 0 8 1 0 1 2 Solar radiation kW m2 Time h Radiation 0510152025 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Wind speed Wind speed m s 0510152025 0 1 2 3 4 5 6 MW T h Electricity Cooling Heat 0510152025 0 5 10 15 20 25 30 MW T h Electricity Cooling Heat 5Copyright 2017 ASME Fig 7 Cooling heating and electricity loads for residential areas on a typical day Result analysis of optimal operation Two cases are studied to show the efficiency of the energy hub based energy system In the first case study focuses on the comparison between the original control method and the suggested optimized operation Cost reduction to the customer and system reliability to the grid after optimization are also addressed Case 2 is about the stability of the suggested interconnected energy hub system The advantage of this approach is that future distributed systems may not require their own separate standby prime mover the sharing of system backup prime movers is realizable Case 1 Optimal operation on typical day In this case energy consumptions of each hub before and after optimization in a typical day are shown Fig 8 describes the pow