Decentralised Energy and Its Performance Assessment Models

The trend of energy development is concerned with not only developing more renewable energies but also transferring from centralised to clean and decentralised power generation. Development of decentralised energy (DE) forms a central part of the world’s energy and economic strategies, and drives the progress towards a more sustainable future. The paper reviews the concepts, development status and trends, benefits and challenges of DE systems, and analyses the existing models and methods for the performance assessment of different DE systems. A hierarchical decision model is then proposed for the performance assessment of DE systems in the framework of multiple criteria decision analysis (MCDA), which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information for a case study with the implementation of the Intelligent Decision System (IDS). In addition, sensitivity and trade-off analyses are conducted to demonstrate the decision making process, which shows how the proposed model can be potentially used to support informed decision making on DE systems.


Energy trilemma and the benefits of decentralised energy systems
'Energy trilemma' is often mentioned in energy industry, which is an encompassing term representing the integrated challenges in energy security, social impact (e.g., energy affordability) and environmental sensitivity (e.g., CO2 emission) as illustrated in Fig. 1. To solve the energy trilemma, sustainable generation and consumption of energy becomes essential in facilitating the world economy while maintaining the current and future generations' welfare, which can contribute in a balanced and holistic way to attaining the overarching goal of energy security from affordable energy supply to environmental protection. On one hand, the traditional model of centralised electricity generation, transmission and distribution has become increasingly difficult to justify its efficiency and sustainability, even though it delivers economies of scale, safety and reliability. For example, the most advanced centralised power station in the UK is estimated to achieve only an energy efficiency of 50% and a further energy loss of 9% can be incurred from the power transmission through the distribution network (Carson et al., 2008). On the other hand, the curtailment of solar and wind energy has also been observed in western China due to insufficient capacity and local congestion of transmission as well as excessive supply during the periods of low demand. To achieve energy sustainability, the requirements for future energy include long-term supply, stable prices, continuous technology improvement and simple installation and maintenance (Omer, 2008). Essentially, sustainable energy development should consider not only cost saving, but also efficiency in energy systems and flexibility of replacing fossil fuels by various renewable energy sources (Lund, 2007). Decentralised energy (DE) and smallscale power grids can be a reliable and cost-effective alternative to large grids, which are more likely to cause failures and inefficiencies. Promoting the use of DE to individuals and local communities can lead to lower energy bills for households, businesses and even industry. In the recent decades, the costs of solar panels and battery storage have been reduced significantly, which provides a basis for producing and consuming energy in a very different way in the future in combination with smart meters and other fast-developing demand side response measures. As a consequence, the trend of future energy development is concerned with not only developing more renewable energies but also transferring from centralised power to clean and decentralised power as illustrated in Fig. 2.
In the resent years, Europe has made a steady progress in making a transition from centralised and largely fossil-fuel or nuclear-based systems delivering electricity to more decentralised energy systems (DG Energy 2008;EU ITRE, 2010), which mostly use renewable energy sources, such as small hydro, wind power, solar power, biomass, biogas Energy security Energy sustainability Energy affordability Energy trilemma and geothermal power. In China, the most polluted cities are mainly caused by the continued use of fossil fuel for heating, industry and transportation, and it is anticipated that the pollution can be reduced considerably by the widespread deployment of DE systems. Fig. 2 Illustrative comparison between centralised power and clean & local power (Source: https://ilsr.org/challenge-reconciling-centralized-v-decentralized-electricity-system/)

The challenges and difficulties of DE systems
In development of renewable energy, it is important to make an informed choice for more efficient, reliable, economical, and environmentally friendly DE systems. However, the development of DE systems is currently not mature, and there are many challenges from different perspectives, which hinders its wider application. On the other hand, relevant policy, legislation and mechanism need to be improved since the implementation of DE systems involves many aspects, such as economic incentives, energy trading management, environment protection and demand side management. In the existing literature, most research focuses on a single renewable energy sector or centralised power network, and the development of DE systems and its potential impact have not yet been studied systematically.
How to evaluate the performance and impact of DE systems, which combine different sources of renewable energy, involves multiple-dimensional considerations (e.g., technical, economic, social and environmental criteria) and remains as a challenging issue in decentralised energy development and policy making. Recently, there has been an increasing amount of research on the use of multiple criteria decision analysis (MCDA) methodology to support the performance assessment of renewable energy systems. The aims of the paper are primarily two-fold: first to review the development of DE systems and summarise the  DTI, 2006), which broadly take into account: (1) electricity generating plants connected to a distribution network rather than a large-scale transmission network; (2) small-scale plants which supply electricity within a local area, and can even sell any surplus back to a distribution network; (3) small-scale installations of solar panels, wind turbines or other renewable energies for local consumption and surplus selling; (4) combined heat and power (CHP) plants where the electricity output is primarily used to serve local consumption or feed into a transmission network, while the heat is often used locally on household, small-scale building or community level; (5) non-gas heat sources such as biomass, solar thermal panels or geothermal energy, for the supply of heat to just one household, a building or a local community. Obviously, different sources of renewable energy can be deployed at a range of different scales from household and building to local community level in accompany with demand-side measures for reducing or shifting energy consumption (Aiken, 2012;EU ITRE, 2010 In addition, the acceptance of local community and their approval of generation capacity is also a prerequisite for developing small-scale DE systems, and it is often challenging to form new disciplines between suppliers and users to achieve the real-time matching of supply and demand.

Development of micro-grid technology to overcome DE challenges
DE can supply users with green power generated from locally available renewable energy resources. However, many interconnected DE systems in a large-scale power network may also give rise to security issues of operation. Micro-grid technology provides an interface to the interconnection of multiple DE systems at different levels (Hatziargyriou, 2015), and can maintain an efficient, safe, reliable, and optimal operation of various DE systems through effective management. Simply speaking, micro-grids can integrate generation, storage, demand-side response and system control together and provide an infrastructure for addressing power security, affordability and sustainability. It is generally featured with a dispersed, locally controlled, independent energy system, which can optimise the real-time matching of supply and demand, can alleviate pressure on a national grid, and is fully compatible with renewable energies. An illustrative structure of micro-grids is shown in As a small-scale distributed system of power generation and distribution, micro-grids can also integrate energy storage, energy conversion, related load monitoring and protection device. It can be not only connected to external grids in parallel but also operated in an isolated environment. In the microscopic aspect, micro-grids generally have the fullyconfigured functionality of power transmission and distribution, which enables local power balance and energy optimisation. The key feature differentiating from a distributed power generation system with load is that micro-grids have the capabilities of both grid-connected and independent operation. In the macroscopic aspect, micro-grids can be thought of as a "virtual" power source or load in the distribution network. Existing research and practice has demonstrated that micro-grids is one of the most effective ways to facilitate DE supply and is of great significance in terms of various social and economic benefits: (1) significantly increasing the utilisation of distributed power; (2) assisting to continuously supply power to critical loads during grid disasters; (3) avoiding the direct impact of intermittent power supply on the power quality of surrounding users; (4) contributing to the optimal use of renewable energy and the energy saving from transmission losses in a centralised power grid.
Currently, micro-grid laboratories and demonstration projects with different characteristics have been launched widely in the United States of America, Europe, Japan and other countries.
There are also challenging issues to be addressed for the operation of micro-grids despite the above benefits. In general, there are multiple energy inputs (e.g., photovoltaic, wind, hydrogen, natural gas) in micro-grids, multiple energy outputs (e.g., electricity and heat), multiple energy conversion units (e.g., optical/electrical, thermal/electric, wind/electric, AC/DC/AC) and a variety of operating conditions (e.g., grid, independent), which makes the dynamic characteristics of micro-grids more complex than a single distributed energy generation system. In addition to the dynamic characteristics of each distributed generation unit, the structure and type of network (e.g., DC or AC) also affect the dynamic characteristics of micro-grids. Therefore, further research should be conducted to address the issues of distributed energy and micro-grids in the renewable energy industry.
In summary, micro-grid is the focus and trend of future decentralized energy development. Most micro-grids not only are hybrid systems, which include different energy resources such as solar, wind and biomass, but also integrate the properties of supply side and demand side. Hence, how to evaluate such complex systems with different aspects balanced adequately by MCDA methods is a key issue in research on assessment of DE systems.

Global development status of DE systems
In the recent years, many countries have been actively seeking the development of As of 2016, the installed capacity of distributed energy in the US is approximately 82.5 Gigawatts (GW) according to International Energy Agency (IEA).
In addition, in order to further promote the development of CHP as a long-term development plan, it was proposed that CHP should contribute 50% of the energy for new office buildings or commercial buildings in 2020, and 15% of the energy supply for existing buildings needs to be converted into CHP. By 2035, the commercial distributed generation capacity will increase to at least 6.8 million kilowatts, ideally to achieve an increase of 9.8 million kilowatts.
Distributed energy in the US is mainly installed in the west coast, east coast and south coast of the US. In addition, distributed energy is mainly based on natural gas and CHP which account for 71% of the energy supply and is distributed in more than 3,700 industrial and commercial projects. Among the applications of distributed energy projects in the US, only 15% are used for cooling and heat in hospitals, schools, hotels and office complexes, and most are concentrated in the industrial and manufacturing sectors, where the chemical industry reached 29%, and the petroleum refining industry reached 18%.

Distributed energy development and planning in Europe
In Europe, Denmark is one of the countries which have achieved very high energy efficiency (European Commission, 2009). In the country, the growth of GDP has not led to increased energy consumption, while the pollution emissions have even fallen considerably.
The main measure is to develop distributed energy vigorously. In Denmark, around half of electricity is generated by decentralised energy systems, more than 80% of the district heating energy is produced by CHP, and the distributed power generation exceeds 50% of the total generated power. For example, the total installed capacity of wind power distributed to their low-voltage distribution network exceeds 3 million kilowatts. The development direction of energy in Denmark is to promote large-scale use of CHP plants with heat storage capacity and to change the fuel of regional district heating plants from coal to natural gas, garbage and biomass. In addition, the Danish government actively supports to build district heating and CHP projects, especially by companies and in remote areas. In addition, more and more CHP projects in densely populated areas use natural gas as fuel, and their thermal efficiency indicators are slightly higher than coal-fired technologies.
Germany is one of the most successful countries in promoting distributed photovoltaic power generation. In terms of distributed energy development, the installed capacity of photovoltaic power generation in Germany reached 41.7GW by the end of 2017, and the main application form was the rooftop photovoltaic power system (Bauwens et al., 2016).

Distributed energy development status and planning in Japan
Due to the scarcity of natural resources, Japan has started very early to promote energysaving and emission reduction technologies in order to maximise energy efficiency. Since 1980, with the operation of the first thermal power unit of the Tokyo National Arena, Japan has vigorously developed natural gas distributed energy, with an average annual installed capacity of 300 MW. The annual installed capacity added is 400 to 500 MW from the 1990s to 2007. Although the domestic investment enthusiasm declined and distributed energy development was also affected by the rising fuel prices and the international financial crisis around 2008, Japan's distributed energy installed capacity reached 9.4 million kilowatts in 2011. Since then, Japan's distributed energy development has slowed down, and the installed capacity exceeded 10 million kilowatts in 2016, of which civilian use accounted for 21% (Narula, 2012).
In Japan's strategic energy plan, the goal of developing and popularising distributed energy is elaborated systematically, which includes CHP, solar power, wind power, biomass and waste-to-energy. Japan's distributed generation is mainly based on CHP and solar photovoltaic power generation, and its distributed power generation projects are developed widely in both commercial environments (such as hospitals, restaurants and public recreation facilities) and industrial sectors (such as chemical, manufacturing, steel and other industries).
According to the Ministry of Economy, Trade and Industry (METI) of Japan, their CHP capacity will reach 16.3 million kilowatts by 2030, including thousands of commercial and industrial distributed power generation projects. Japan aims to generate 20% of the total electricity supply by distributed energy systems by 2030. Photovoltaic power generation is widely used for both residential rooftop photovoltaic and public facilities such as parks, schools, hospitals and exhibition halls (Global DER Deployment Database 3Q20, 2020).
Japan is also the market leader in development of micro-grids. The new energy and industrial technology development organization in Japan have facilitated R&D and demonstration for many micro-grid projects globally.

Distributed energy development status and planning in China
According to the development summary of distributed energy in China in 2017, the growth rates of gas-fired power, wind power, small hydropower and photovoltaic power generation vary considerably. The cumulative installed capacity of gas-fired power generation reached 87.93 million kilowatts with an annual increase of 13.99%, the cumulative installed capacity of wind power was 188 million kilowatts with an annual increase of 11.7%, and the photovoltaic power generation was the fastest growing renewable energy. According to the '13th Five-Year Plan for Power Development', the total installed capacity of gas-fired power generation in China will reach 110 million kilowatts in 2020, of which the CHP supply will reach 15 million kilowatts. The '13th Five-Year Plan for Photovoltaic Development' proposes that the total installed capacity of photovoltaic will be 150 million kilowatts by the end of 2020 (Wu.,2018). In summary, on account of lower costs, better energy policy and increasing attention to renewable energy, global distributed generation is expected to show a rapid growth trend in the next few years. In the US, Europe and many other developed countries, distributed power generation has already contributed to a high proportion of the total energy generation.
Although the growth rate of distributed energy development is expected to slow down in these developed countries in the future, the new investment boom of distributed energy will appear in emerging markets such as Asia Pacific and South America.

MCDA application in renewable energy evaluation and assessment
In order to support informed and insightful decision making, it is necessary to evaluate the performance and impact of various renewable energy systems systemically which involves multi-dimensional aspects and performance factors. Many researchers have developed a spectrum of different criteria, methods and models for impact assessment of DE systems. The following literature review is conducted in terms of four different application areas (Wu et al., 2017).
(1) Renewable energy planning and policy making Diakoulaki et al. (2007) used MCDA method to analyse the relative importance of different attributes and features of desired energy efficiency for supporting energy policy making. Lee et al. (2008) exploited the fuzzy theory and analytical hierarchy process (AHP) to select criteria for analysing the competitiveness of national energy policy making in Korea.
Mahdy and Bahaj (2018) explored a method of combining AHP with geographical information system (GIS) for assessing the development potential of offshore wind energy in Egypt. Köne and Büke (2007) applied analytical network process (ANP) to formulate multiple independent attributes to determine the best technology for power supply in Turkey.

Typical MCDA methods used for DE assessment
In the above applications of MCDA to renewable energy, typical MCDA methods can be categorised to the following three categories.
(1) Methods based on functional model Based on MAUT, Bayesian inference and Dempster-Shafer theory, the evidential reasoning (ER) approach has been developed under the principles of probabilistic inference and evidence-based decision making for dealing with MCDA problems under various types of uncertainty, including ambiguity and randomness (Yang, 2001;Yang and Xu, 2002). It uses a belief structure to represent both quantitative and qualitative criteria consistently, a belief decision matrix to formulate a MCDA problem under various types of uncertainty, and the evidential reasoning algorithm to enable probabilistic inference for aggregating multiple criteria to generate overall distributed assessments. The further advance of the evidential reasoning rule provides a unique method for combining multiple pieces of independent evidence conjunctively with weights and reliabilities (Yang and Xu, 2013). The ER approach requires that the assessment of a renewable energy system on any criterion be independent of its assessments on other criteria. In other words, the assessment standard of any criterion for a renewable energy system should be independently determined, irrespective of whether its assessments on other criteria are known or not. This condition is more realistic and easy to satisfy and check than those for many other MCDA methods, such as the additive preferential independence condition for MAUT and AHP methods.

Selection of main criteria
(1) Technical criteria Technical feasibility and effectiveness are the fundamental criteria for the assessment of renewable energy systems. We can use thermodynamics to assess how effectively and efficiently a renewable DE system works. Primarily, the technical criteria, such as technical maturity, safety, reliability and self-sufficiency should be considered (Chatzimouratidis, 2008;Madlener et al., 2007;Mamlook et al., 2001;Twidell and Weir, 2015;Wang et al., 2009).
(2) Economic criteria In order to maintain economic sustainability and opportunities, it is necessary to consider the affordability and accessibility of renewable energy systems. In general, there are key attributes to be considered in the economic category, which involve initial investment, construction time, operation and maintenance costs, payback time and cycle of service life (Ahmad and Tahar, 2014;Doukas;2007;Karakosta et al., 2013;Wang et al., 2009).
(4) Environmental criteria Sustainable development aims to overcome a series of economic, energy and environmental problems, especially the global environmental pollution and the unbalanced relationship between economy, energy and environment. With the intensification of the environmental protection situation, there have been more requirements for the environmental efficiency evaluation of energy than ever before. These problems provide new directions for relevant decision making problems (EC, 2003;Lawrence, 2007). The stakeholder mapping approach (Mitchell et al., 1997) as illustrated in Fig. 5 can be used to analyse environmental impact assessment. Typical environmental impact factors should be considered for various renewable energy systems which include CO2 emissions, SO2 emissions, land use, noises, exposure to electromagnetic field and visual impact (Haralambopoulos and Polatidis, 2003;Lawrence, 2007;Løken, 2009;Wang et al., 2009).
All of the above criteria or factors which are used to assess the performance of renewable energy systems should be identified in a consistent and systematic way.
Furthermore, the relative importance of each category and its impact factors need to be taken into consideration.

Technical criteria (1) Maturity
Definition: Maturity is often used to evaluate the technology itself, and the degree of maturity can be approximated by whether this kind of technology has been widely adopted at regional, national and international levels. This measure also indicates whether the technology has reached its theoretical efficiency limit or it can still be improved further. In practice, it can be considered whether the technology is only tested in the laboratory setting, performed in some private companies, used in a wide range yet with a potential of technology improvement, or has reached its maturity and theoretical efficiency limits (Beccali et al., 2003). The assessment grades can be defined as follows,

Assessment grade:
(1) Technologies only tested in laboratory (Immature); (2) Technologies only performed in demonstration projects with the goal of experimenting the operating and technical conditions (Poorly mature); (3) Technologies increasingly applied with the scope of further improvement (Mature); (4) Technologies that are consolidated and are close to the theoretical limit of efficiency (Sufficiently mature).

(2) Safety
Definition: Safety is concerned with the very basis that people who work in the power plant can be guaranteed of safety and the infrastructure will not be damaged. There are two generic safety indicators. One is the specific power generation accidents, accounting for the proportion of total power accidents (PA), and the other is the proportion of casualties by accidents to the total number of casualties (PC) in the previous year. In a hybrid power system, an additive function can be used to calculate PA and PC, i.e., for a hybrid system which includes multiple types of energy resource (Morris A.S., 2012). An illustrative set of assessment grades can be defined as follows,

(3) Reliability
Definition: The term of reliability has a range of different definitions. A generally reliable power system is able to provide uninterrupted power supply to meet the demand with acceptable quality standards. Power system reliability can be broken down into two basic aspects of system adequacy (or static reliability) and system security (or dynamic reliability).
System adequacy relates to the existence of sufficient facilities within the system to generate sufficient energy to satisfy the consumer load demands and to meet the operation constraints of power transmission and distribution (Amjady, 2004). Adequacy is mainly concerned with the static conditions, while security relates to the ability of the system responding to dynamic or transient disturbances or faults arising within the system, which is associated with the conditions where both local and widespread disturbances and the abrupt loss of major generation or transmission facilities can potentially lead to dynamic, transient, or voltage instability of the system (Amjady, 2004).
In practice, the static reliability can be measured by the unavailability duration of the system (UDTS), which represents the reliability of equipment based on the mean time to failure (MTTF) of each main component (Michael F., 2015). For example, if UDTS is smaller than 8 days per year, the system will be considered as statically reliable. If UDTS is greater than 8 days per year, it will be considered as statically unreliable.
Assuming that the load level requires the normal reliability of power supply, there are two other reliability indicators: loss of load frequency (LOLF) and loss of load expectation (LOLE). In general, the LOLE of reliable energy system is from 0.1 to 5 days per year.
Practical and theoretical research findings can be used as guides for the assessment of system reliability. For example, (i) Wind and photovoltaic hybrid power generation have excellent complementary benefits; (ii) If it is only powered by wind power or photovoltaic, the system reliability will deteriorate when the capacity is greater than 500MW; (iii) When the installed capacity of the hybrid system is small, there is no obvious advantage. Reliability can be improved when the installed capacity reaches a certain level; (iv) For the hybrid systems which include wind power, photovoltaic and energy storage, when the proportion of photovoltaic is large, the change in energy storage capacity has high impact on system reliability; (v) When the installation capacity of a system is less than 400MW, the access to renewable energy can alleviate the insufficient power supply of the system, reduce the probability of extreme conditions, and improve the reliability of the system. When the capacity is more than 400MW, the impact on system reliability is related to the access point by which the DE system is connected to the national grids.
(4) Self-sufficiency Definition: The degree of self-sufficiency can be measured by the ratio between the total generation capacity of a system and the maximum load of consumption. Taking into account the characteristics of DE systems and micro-grids, the optimal situation is that the total power generation can meet demands and consumed by local load (Ruppert-Winkel C., 2014). Therefore, we can give an ideal range of 0.8-1.0 on this criterion.

Economic criteria
(1) Investment cost Investment cost for DE systems comprises of all costs relating to the purchase and installations of mechanical equipment, engineering services, construction of roads, connections to the national grid, and so on (Wang et al., 2009). Further operation and maintenance costs are not normally counted into investment costs. Investment cost is the most commonly used economic criterion to evaluate DE systems. (

2) Operation and maintenance cost
Operation cost includes employees' wages and the funds spent for energy, products and services associated with the operation of an energy system. Maintenance cost is used to prolong energy system life and avoid failures. Maintenance cost is much less than the financial losses incurred from the failure of an energy system and maintenance also increases the credibility and confidence index of an energy system.

(3) Payback period
Payback period means the time period needed to repay the lump sum of investment back to the investors. This criterion is usually used to assess the profitability. From a financial perspective, investors always favour projects with short payback periods over those with longer ones.

(4) Service life
Service life is the expected lifetime for a system that can be functional properly, i.e., how many years the system can be on service. Normally, service life is featured as a U-curve.
At the beginning period of a system, it is more likely to fail, before the system reaches a stable condition. Later in the life cycle, the system becomes more likely to fail again. Projects which have a long service life and a short payback period are undoubtedly more competitive in attracting investment.

(5) Construction time
Construction time captures the time from the beginning to the end of constructing an energy system. The length of construction can somehow be considered as the degree of difficulty of implementing the energy system.

Social criteria
(1) Social acceptability Social acceptability measures the overall opinion related to an energy project primarily by the local population to be affected (Kaya and Kahraman, 2010). It is important since the opinions of the local population and pressure groups may heavily influence the amount of time needed to complete an energy project. The qualitative criteria of social acceptability can be evaluated from surveys or focus group meetings. For example, a rating between -2 and +2 can be used to reflect the population's expected attitude to the occurrence of new power plant technologies in the local region (Brand and Missaoui, 2014). A zero score can be given to the (2) Social benefit Social benefit can take into account a range of things, like job creation, tax redemption and income generation, to be brought to the local region by introducing an energy project, especially in less developed regions. This criterion can be recapitulative in the assessment.

Environmental criteria
(1) CO2 emission reduction CO2 emission reduction is one of the most important considerations for the development of DE systems. It is a quantitative criterion and can be calculated approximately.
(2) Land use (KM2/1000MW) Every energy power plant needs to use land, which will lead to environmental and landscape change due to the land being occupied by the energy power plant. This criterion could be regarded broadly as a social impact criterion. (

3) Noise
Noise pollution generated from energy power plants can be quite disturbing. Noise can be caused by aerodynamic and mechanical sources, and can be disruptive to animal life as well as human life. Noise pollution not only affects the environment, but also damages human physiological heath, as human can suffer from hearing loss if they are exposed to a very noisy environment for a long time. Noise may also cause operational accidents indirectly.
Sound pressure level can be used to measure noise levels in residential areas (Walker and Jenkins, 1997). This criterion can be measured quantitatively in dB. In general, noise levels must be lower than 45 dB in proximity of residential areas.

(4) Visual impact
Visual impact reflects the visual nuisance that may be caused by the development of an energy project in a specific area (Wang et al., 2009). It is often used to evaluate alternative solar and wind energy plants. The evaluation of visual impact for alternative DE systems can involve the landscape of different sites, distance from the nearest observers, the type and size of plants to be installed and the possibility to integrate them with their surroundings.

(5) Renewable penetration
Renewable penetration refers to the percentage of electricity generated by a particular renewable resource (Wu et al., 2019). It can be quantified by the percentage relative to the total amount of electricity either generated or consumed.

A case study of a micro-grid project in an industrial park
The case study is concerned with a large micro-grid project constructed in an industrial park in China. It is a multi-vector DE system which integrates solar panels, wind turbines, storage and diesel backup. The solar panels and wind turbines are installed on the roofs of all the buildings, and a battery storage system is included for adding into the combined system as the characteristic of intermittent power supply of solar and wind energy. The whole system is integrated into an intelligent management platform of power utilization, so that it can coordinate and integrate multi-vector energies efficiently. Since the system is off grid and has its own distribution network, the power generation can be consumed locally so as to reduce the load in peak hours and improve the efficiency of final energy consumption. Initially, this project was launched for demonstration purpose without performance modelling and decision analysis.
Specifically, the hybrid distributed energy system includes 400KW roof photovoltaic, 100KW carport photovoltaic, 10KW wind energy, 450KW*2h Lithium-ion battery and 1500KW diesel generators for backup. In order to analyze the performance of different hybrid energy systems and then get the best choice, four different alternatives have been proposed: A1, A2, A3 and A4. A1 has only 600KW photovoltaic, A2 is a combination system of 500KW photovoltaic and 10KW wind energy, A3 include an additional energy storage into A2, and A4 add another diesel generator into A3 for backup.
According to the survey data of this project and the assessment framework, the detailed information of each criterion in each alternative is shown in table 1. Four top level criteria are divided into 15 sub-criteria. In order to generate the weights for these sub-criteria, the direct assignment method is also used according to the stakeholders' opinions. The weights for different sub-criteria are summarized in Table.2 The decision problem is then analyzed on the basis of the MCDA model described in Section 4. The analysis results generated by using the Intelligent Decision System (IDS) (Xu and Yang, 2003) are shown in Fig. 6 and Fig. 7. In Fig. 6, A4 is ranked the first, which includes PV, wind, battery storage and diesel generator. A3 is ranked the second, which includes PV, wind and storage. It can be observed from Fig. 7 that the single PV system A1 has the best performance over the economic criteria, given the fact that A1 has the shortest construction time and payback period. However, it has the poor performance in the technical criteria as it has a rather low reliability due to intermittent power generation.
Alternative A4 is a hybrid system and outperforms other three systems in not only the overall performance but also the top-level technical criteria. Assuming that the weight of the technical criterion is relatively high, a change in the weight of the criterion leads to a change of the overall performance ranking. With the change of the weight of the technical criterion, a balance point is found in the process. In particular, alternatives A1 and A2 outperform A3 and A4 in the top-level economic criteria, but their overall performance is lower than that of the other two alternatives since the weight of the economic criterion is relatively low.
Similarly, a change in the weight of the economic criterion also leads to a change of overall ranking and performance as shown in Fig. 8. Therefore, the weight elicitation of each criterion is very important for solving the MCDA problem, and the generation method needs to consider each alternative with specific preferences and judgments from different stakeholders. Different weights affect directly the decision outcome of alternative energy systems.
The sensitivity and trade-off analysis are included in Fig. 8 and Fig. 9 respectively.
In this case study, trade-off analysis is conducted between any two top-level criteria or lower-level criteria. In Fig. 9, for example, the reliability is chosen as the lower-level criterion and economic criteria as the top-level criteria for trade-off analysis, which shows clearly that A1 has a quite high economic performance but rather low reliability, while A4 has very high reliability. Similarly, we can also choose any other two top-level or lower-level criteria to do trade-off analysis. The trade-off analysis is closely related to the preferences of stakeholders. Fig.6 The ranking of four alternatives on overall performance

Conclusions
DE has already been regarded to be one of the most effective solutions for solving the energy trilemma problem and it is thus very important to model and assess the performance of alternative DE systems systemically. In general, the assessment of DE systems is considered as a complex MCDA problem, which needs to take into account technical, environmental, economic and social aspects. In this paper, the literature review provides a holistic overview on the trend of future energy development in the situation of energy trilemma, the importance of DE systems, in particular multi-vector decentralised renewable energy systems, and the challenges and difficulties in the performance assessment of DE systems. According to the specific nature and characteristics of DE systems, this paper further presented performance modelling and multiple criteria decision analysis models for multi-vector decentralised renewable energy systems. The proposed model was applied to a case study of selecting alternative micro-grid energy systems in an industrial park in China.
The decision results, including sensitivity and trade-off analysis, demonstrated how the proposed MCDA model can potentially be used to support informed decision making on alternative multi-vector decentralised energy systems. Future research will be conducted on identifying more granular performance indicators in real-world applications, and quantifying the relationships and dependence among criteria in value or utility functions, which have not been studied in existing literature. In addition, more real case studies will be identified for different decentralised renewable energy systems in difference regions or countries.