Technological development of multi-energy complementary system based on solar PVs and MGT

Abstract

The complementary micro-energy network system consisting of solar photovoltaic power generation (solar PVs) and micro-gas turbine (MGT), which not only improves the absorption rate and reliability of photovoltaic power, but also has the advantages of low emission, high efficiency, and good fuel adaptability, has become one of the most promising distributed power systems in the field of micro grid. According to the development of current technology and the demand of actual work, this research described the domestic and foreign development of micro-energy network system based on solar PVs and MGT. Moreover, it analyzed the challenges and future development regarding the micro-energy network system in planning and design, energy utilization optimization and dispatching management, and system maintenance, respectively. Furthermore, it predicted the future development of the key technology of the multi-energy complementary system. These results will be beneficial for the progress of this field both in theory and practice.

Keywords

renewable energy / solar photovoltaic power generation / micro gas turbine / multi-energy complementary system / micro-energy network

Introduction

With the rapid development of the global economy, energy shortage and environmental problems become increasingly serious. How to ensure sustainable coordinated development of energy supply and minimum environmental pollution has become a common concern in the world [¹,²].

As a solution for accessing the new energy resources, the concept of micro energy network was proposed in the early 21st century [³–⁵]. From the view point of system, micro energy network is a controllable unit that supplies power and heat to consumers in combination with micro sources, loads, energy storage equipment, and control devices. Micro power source, a small plant unit with the power electronic interface, is the main power supply in the micro-energy network. It includes wind turbines, photovoltaic cells, micro gas turbines, fuel cells, etc. It is worth noting that, the micro energy network not only can operate when connecting to the large grid, but also can run in island mode when faults happen or as necessary [⁵]. It is known that the photovoltaic system is one of the cleanest, most practicable, and most promising resource in all kinds of renewable energy resources nowadays. However, the output of photovoltaic power is greatly affected by the weather condition because of its obvious intermittence. To increase the reliability of photovoltaic power generation and reduce its impact on the main grid, it is necessary to add controllable micro power sources (such as micro-gas turbine, etc.) to it. Moreover, researches show that micro gas turbine is considered as the most promising distributed power for supplying the cooling, heating, and power simultaneously, due to its low emissions, and good fuel adaptability. Therefore, the study of the development and key technologies of the micro energy network system composed of solar PVs and MGT will have theoretical significance and vital application value [²,⁶,⁷].

At present, researches on multi-energy complementary system of solar PV and MGT have been conducted in the United States, European Union, Japan, and China, etc. Those researches mainly focus on the following aspects of model establishment and solution [⁸–¹⁰], operating characteristics and control methods [¹¹–¹³], intelligent optimization strategies and loads predictions [¹⁴–¹⁶], operation management and engineering demonstration [¹⁷–¹⁹], and etc. However, most of the research achievements have been focused on the single power generation unit in the micro energy network, such as solar photovoltaic power generation technology, natural gas distributed generation technology, CCHP technology, energy storage, and power storage. Studies on the integral planning design and the operation key technologies of the multi-energy complementary system are still few and far between. Meanwhile, it is indicated that there are great challenges in the integrated modeling, mechanism analysis, energy utilization optimization [⁶,⁷,²⁰], etc., which seriously restricted the demonstration and large-scale commercial application of such complementary systems.

Therefore, this paper is aimed to review the current development of the available technological alternative to the multi-energy complementary system. The focus is placed on those described technologies suitable for the system to combine with solar PV and MGT to improve the renewable energy conversion efficiency and to lower the system operation risk. The review not only considers system integration design and optimization technologies, but also energy management and control technologies that can guarantee the safe operation of system and feed the real-time operation information back to the system underlying models. Besides, the future development is predicted. The results are very beneficial for designers and users to select the most appropriate technology for reasonable operation of a micro-energy system.

Technology of multi-energy complementary system

Planning and design of integration system

Compared with the traditional power grid, the complexity and uncertainty of a multi-energy complementary system are greatly increased, which are reflected in the following aspects.

Certainty and uncertainty of micro-energy system

The system certainty analysis indicates that the information of wind, solar and load requirements involved in the integration system planning and design process are derived from the historical record data [²¹–²³]. While, the uncertainty analysis mainly refers to the modeling of renewable energy and load variations based on probability statistics [²⁴–²⁷]. Since solar power in the micro-energy system is intermittent and random, its availability depends on several factors such as natural conditions, climate, and the environment. Figure 1 shows the power distribution of micro energy power system of PV and MGT, which is significantly affected by solar fluctuations in one year. Meanwhile, huge uncertainty characteristics also exists in cold and heat loads of users due to user types, work (living) habits, seasons, changes of day and night, etc.

System mathematic model

In term of system model, the time scales of the subsystem dynamic characteristics in the multi-energy complementary system are quite different. Therefore, it is difficult to carry out simulation analysis and coordination control with the same time scale, and to establish accuracy mathematical model [²⁸,²⁹]. On the one hand, for the gas turbine, the dynamic characteristic of inertial delay exits. It is interesting to note that, this characteristic is also more obvious in the steam power generation. Therefore, this kind of dynamic characteristic should be described with a nonlinear differential equation, with involving start-up, operation, disturbance and other dynamic links [²⁸]. On the other hand, the network output power needs to be instantaneously balanced, which requires that the dynamic characteristic should be described by differential-algebraic equations. However, for other thermal equipment, the cold and heat conversion processes are relatively slow, and their dynamic characteristic is usually expressed in terms of minutes and hours [²⁹]. Meanwhile, due to the fact that the subsystems have the characteristics of nonlinearity, strong dynamic coupling and complex processes, using accurate models for all subsystems to perform detailed simulation under a unified framework is a very time-consuming process, and the calculation accuracy also cannot be guaranteed. Therefore, it is necessary to take the dynamic characteristics of the components into consideration to complete the system integration design and modeling.

System optimization

As for the system optimization, Xiao et al. [³⁰] and Weng et al. [³¹] studied the various application scenarios of the distributed generation system by using software PDMG. In their software system, the functions such as intermittent data analysis, distributed power system planning and design, and energy storage system planning were realized. What is interesting is that the target curve, inverter structure design, and hybrid energy storage design could be automatically generated according to the scene. Guo [³²] presented a study on Newton-Raphson method for the power flow calculation of micro energy network. By using this method, the suitable access capacity for micro energy network, penetration rate, voltage level, and the recommended connection mode were obtained, considering the load change. The National Renewable Energy Laboratory in the United States developed a kind of software named HOMER for the micro grid. They set the lowest life cycle cost of micro grid as the optimization target, and the used enumeration method to determine the optimal capacity allocation of distributed power [³³]. Reference [³⁴] studied the planning design of low-permeability grid-connected multi-distributed power grids. The objective functions for minimizing the investment and operating costs, active power loss and load node voltage offset of distributed power sources were constructed. The multi-objective chaotic quantum genetic algorithm was proposed based on the integrated real number coding and multi-objective optimization strategy. Mehleri et al. [³⁵] researched the planning design of a distributed cogeneration system with photovoltaic arrays. They carried out an optimization design method of heat pipe network to verify the correctness of the model. Siddiqui et al. [³⁶] investigated the minimization cost problem of the micro energy network by introducing carbon emission costs to optimize the deployment of micro energy network and cogeneration machines. The results indicated that the potential benefits of the micro energy network technology can mitigate climate change. References [³⁷,³⁸] investigated the variation operation performance of distributed complementary energy system of gas turbine and solar photovoltaics, as illustrated in Fig. 2. The results showed that the local consumption of renewable energy greatly improved the electrical efficiency of the distributed energy system.

System integration

For system integration, the principle of scientific energy utilization should be followed to optimize resource allocation and to improve the efficiency of comprehensive energy utilization. Jin et al. [³⁹] studied and summarized the theory of the distributed cogeneration system of cooling, heating, and power with multiple energy sources, and established the theory of comprehensive cascade utilization of energy. Besides, it made significant progresses in core technologies such as complemented thermal chemistry of fossil fuels and solar. Liu [⁴⁰] researched the integration system with multi-energy complementary and waste heat cascade utilization, and discussed the impact of innovation unit technology on the energy saving rate of the entire system. Jiang [⁴¹] studied the utilization of available energy in each unit in the system of combined cooling, heating and electricity based on internal combustion engine, and explored the coupling mechanism between the positive-negative coupled cycle and absorption dehumidification reverse cycle of the combined cooling and heating generation system.

In conclusion, the optimization objectives of the planning and design scheme proposed in the current study are not flexible and comprehensive enough, and most of them lack network modeling. At the same time, there is a lack of reliability modeling for power grid and system components. The advantages of distributed energy systems are reflected in many aspects such as technology, economy, environment protection, and society, which leads to the fact that the evaluations are needed in terms of reliability, life cycle cost, energy utilization efficiency, levels of pollutants, greenhouse gas emissions, and fossil fuel consumption. Therefore, the system planning design includes not only the optimal capacity configuration, but also the optimal network structure, the location of distributed energy sources, and the design of comprehensive evaluation network.

Technology of energy management and control

With the increasing penetration rate of new energy and intelligent load, the energy system dispatching and energy management also face a series of challenges.

Energy management system

It is important to note that the environmental goals, safety goals, and their combinations should be considered to maximize the utilization of renewable energy and the benefit of distributed generation systems. Due to fact that the energy management system of the distributed energy system is usually small-scaled, the optimal use of energy and the long-term economical reliable operation of the system could be obtained by managing power supply and loaded operating conditions, and applying active and reactive power commands to controllable distributed generation and storage units [²⁷,³⁴]. Figure 3 is the topology structure of energy management system for distributed power system, which is composed of a real-time simulator, power exchange, a DC/AC converter, an AC/DC converter, PLC, batteries, and so on. In the long-term, more considerations are given to economy and security, including effect of environment, power generation costs, controllable loads within the system, and information of the main grid. For energy management in the short-term, the response time is relatively much faster in the time period of minutes or even seconds, including frequency adjustments in the system, distributed power supplies, and energy storage devices. Therefore, the main functions are required to include load prediction, energy exchange with large main grid, distributed power unit output, and demand-side response to load, etc. [⁷,¹⁶]. Whitney [⁴²] built thermal management subsystems and carried out implement energy management for a cooling, heating, and power generation system with fuel cell. By optimizing the parameters of different heat exchangers, the primary energy efficiency of the system could reach its maximum value.

Layers of energy management system

The energy management system is usually divided into three logical layers: the optimization layer, the management layer, and the equipment layer [⁵,²⁰]. The equipment layer refers to the control components belonging to the individual equipment of the system. For example, the micro gas turbine and other equipment such as the PV inverter are classified as the equipment layer. The equipment layer is where each equipment controls itself. The management layer refers to the whole network system, such as the controller, the measurement, and the control device. The optimization level actually refers to the top layer including the basic models of the system and component, the renewable energy generation and load forecasts, and the control strategy of distributed power system. The final implementation of the whole system relies on the distributed energy control strategy and the energy optimization management method in the top layer. The device layer has a fast response time with a range from a millisecond to a second level. Usually, an optimization layer has a longer response time at an hour level.

In this field, lots of investigations have been conducted. Rodriguez-Aumente et al. [⁴³] put forward a regional heat and cold supply system for commercial buildings and conducted the optimization for the system configuration. The results show that the heat energy and the heat-driven absorption refrigeration of the internal combustion engine can reduce the power demand of the building and avoid the overload operation of the internal combustion engine. Gu et al. [⁴⁴] and Masatoshi et al. [⁴⁵] studied the optimal operation strategy of the thermoelectric power system including PV and wind power, and investigated the effect of the uncertainty performance of the electric load on the optimal operation by using the chance-constrained programming method. Taher et al. [⁴⁶] proposed a new multi-objective fuzzy adaptive hybrid particle swarm optimization algorithm to study a distributed power system based on fuel cell. The aim of the problem is to reduce the total power loss, the power cost, and the total pollutant generated by the fuel cell and the substation bus. Piperagkas et al. [⁴⁷] proposed a particle swarm optimization (PSO) algorithm to solve the optimal operation problem of the thermoelectric system with wind power generation, which considered the randomness of wind power generation and user load. They set the minimum operating cost and minimum pollutant emission as the optimization objective function. Chen et al. [⁴⁸] studied the multi-objective optimization and management problem of micro-power grids with multiple distributed generation units, including wind turbine, micro gas turbine, fuel cell, photovoltaic cell, and storage battery. The genetic algorithm was used to solve the above-mentioned problems.

Control mode

There are mainly two kinds of centralized and decentralized control modes for the energy management system. The centralized control mode consists of the upper central energy management layer and the bottom local equipment controller layer for distributed power source and load etc., which requires mutual bi-directional communication between the two layers. The upper central energy management layer can realize information interaction with the power grid dispatching system, formulate the real time optimal operation strategy and release the control instructions to the bottom equipment controller layer based on market price information, intermittent power output prediction in distributed energy system, and load forecasting results, which is displayed in Fig. 4. With this mode, the operation strategy is obtained according to different optimization targets and constraints integrated with the response of the demand side. When the decentralized control mode is adopted, the energy optimization task of the distributed power system is mainly accomplished by the decentralized equipment controller. The main function of each equipment controller is not to maximize the efficiency of the individual equipment, but to collaborate with other equipment in the system to improve the efficiency of the whole system.

Murai et al. [⁴⁹] proposed an operation control strategy for the regional combined cooling and heating station based on the electric-driven heat pump, taking the system operation cost as the optimization objective. Ono et al. [⁵⁰] simulated the optimized operation of the terminal equipment in order to improve the energy utilization ratio of the regional power supply system, including the pipeline resistance, the optimization operation of refrigerating unit and energy storage system. Zhang [⁵¹] established the optimization model of the cogeneration system based on the gas turbine and absorption chiller and proposed the optimal control strategy for the cogeneration system under different operating conditions with the lowest operating cost as the optimization objective.

The basic control methods usually include PQ, VF and droop control methods [¹¹–¹³]. The PQ control method is commonly used in the distributed power system, as depicted in Fig. 5. The VF control method is adopted for the energy storage system or the main power unit. Both control methods can be used in the micro-grid network. However, the development of the micro-grid should also consider flexibility, especially plug and play and the arbitrary variation of micro-power or load in the future.

Technology of monitoring and protection

The access of multiple distributed power and energy storage devices in the micro power network has completely changed the fault characteristics of the distribution system, which makes the variations of the electrical quantity very complicated. The traditional protection principle and the fault detection method will be greatly affected, which may make the location of fault hard to be judged accurately. At the same time, the electricity quality problems will be caused such as voltage fluctuation, overvoltage, under voltage and power harmonics. The nonlinear load fluctuation of the distributed power system and electronic equipment will adversely affect the safety and stability of the power grid [⁵²–⁵⁶].

System monitoring

Yuan and Zheng [⁵²] conducted are search on the Hilbert-Huang transform (HHT) method and adopted the HHT method to detect power quality of micro-network. Aiming at the problem of the endpoint flying wing and the mode confusion in the empirical mode decomposition (EMD) process, they proposed a corresponding improvement method to strengthen the analysis ability of the HHT. The results showed that the improved HHT method could effectively detect the power quality problems such as voltage fluctuation and power harmonics in micro-grid. Reference [⁵³] presented a comprehensive evaluation method for micro-network power based on the improved analytic hierarchy process (AHP) and the gray theory to solve the problem of the lack of software for real-time monitoring and comprehensive evaluation of power quality in the distributed energy supply system. This platform developed an intelligent system which could realize real-time monitoring, reasonable evaluation, and comprehensive power quality management. Wang et al. [⁵⁴] constructed a multi-objective distribution network reconfiguration model, which considered the uncertainties of wind power, photovoltaic power generation and load variation, and optimized three important evaluation indexes in distribution network including active work loss, minimum voltage value, and load balance. The results showed that the algorithm could quickly achieve the decision solution of the multi-objective reconfiguration model for the distribution network with a high searching efficiency, which verified that the network reconfiguration could obviously improve the performance indexes of the distributed power system.

Systematic self-protection

The self-protection of distributed energy system is another important research aspect. The self-protection for the distributed energy supply system can be divided into public connection point (PCC point) protection, internal main / branch lines protection, and micro-source protection from the mutual relationship and logical level. Based on this principle, Zhu et al. [⁵⁵] put forward the concept of hierarchical protection. The corresponding protection strategy was configured according to the importance of protection area. The domestic and foreign research was mainly focused on the protection configuration and protection coordination of these three areas. The supply ability of tidal current was related to its control strategy for different types of power systems. Therefore, the bidirectional flow characteristics are determined by the internal structure of the distributed energy supply system. It is worth noting that the protection requirement of the distributed energy supply system is difficult to be satisfied by the selectivity principle in the traditional protection method, especially for the photoelectric power source with a big fluctuation of power supply. It is not possible to adjust power generation according to the load requirement by the adding of the energy storage device, since it requires a large capacity of energy storage devices, which will greatly reduce the economy of the system. This kind of distributed micro-network power system will generally meet the feature of ‘plug and play’, which will increase the uncertainty of the current flow in the system. Therefore, protection methods should be designed to be as unaffected as possible by the current flow.

The key characteristic of the distributed energy supply system in operation mode is that it can operate in both network and stand-alone mode. This feature requires that the protection method in both modes should function properly, which poses new challenges to the design of the protection system. In stand-alone mode, the short-circuit current is provided by a DG and the DG based on an inverter is unable to provide a large enough short-circuit current. In network mode, the short-circuit current can be analyzed by the superposition theorem and supplied by the power grid. The short-circuit current supplied by the inverter DG only occupies a small portion of the short-circuit current. The former can be set by the calculation of fault current in different operating modes, which is relatively easy to be realized. However, the protection becomes complicated due to the restriction condition. The latter can be satisfied by a set protection method for different operating modes. However, the requirements for protection adaptability are relatively high.

In addition, the structure of the distributed energy supply system is complex, and the multiple power source structure makes the fault judgment more complicated. Sometimes more information is needed to ensure that the fault can be removed in time. Therefore the protection for the communication is necessary.

Summary

The transformation of energy supply mode from centralization to distribution is the future development trend and the only way to realize the transformation of the energy system. It is expected that the distributed energy system will be ubiquitous in year 2050, and a complementary micro energy network composed of solar PV power generation and micro-gas turbine will become one of the main energy supply power systems. In summary, there are many researches focusing on the planning and design of micro-power grid, but there are still many key technologies that need to be further studied and systematized. The multi-energy complementary micro-energy network system based on photovoltaic power generation and gas turbine is very different from the conventional power grid in both network mode and stand-alone mode, such as its power composition, structure, operation mode, which makes it sustain the unique characteristics in areas including planning and design, protection and control, operation optimization and energy management, simulation analysis etc. Therefore, it is necessary to adopt proprietary methods and techniques to develop them.

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