Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process

Renewable Energy xxx (xxxx) xxx

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Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process Keiko Hori a, *, Jaegyu Kim b, Reina Kawase b, Michinori Kimura b, Takanori Matsui c, Takashi Machimura c a

United Nations University Institute for the Advanced Study of Sustainability, 5-53-70 Jingumae, Shibuya, Tokyo, 150-8925, Japan Lake Biwa Environmental Research Institute, 5-34, Yanagigasaki, Otsu, Shiga, 520-0022, Japan Division of Sustainable Energy & Environmental Engineering, Graduate School of Engineering, Osaka University 2-1, Yamadaoka, Suita, Osaka, 565-0871, Japan b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 April 2019 Received in revised form 28 October 2019 Accepted 17 November 2019 Available online xxx

When developing a sustainable local energy system, it is useful to apply backcasting to help select an appropriate renewable energy mix based on an evaluation by diverse stakeholders of multiple possible implementation impacts. The purpose of this study was to propose a co-creative design support method for local energy systems that includes (1) participatory development of a local future vision, (2) quantitative projection of future energy demand coupled with future vision, (3) multi-objective optimization of a regional renewable energy mix consistent with the future vision, and (4) a co-creative optimization process that encompasses local resident preferences. A case study in Takashima, Shiga Prefecture, Japan, was conducted in collaboration with the Takashima Community Promotion Council to test the proposed method. A participatory workshop was conducted with nine officers and 16 citizens to design a qualitative future vision for 2040. This vision was then quantified and the future energy demand was projected using the Extended Snapshot Tool model. Pareto solutions for an optimal renewable energy mix were visualized using the Renewable Energy Regional Optimization Utility Tool for Environmental Sustainability with a multi-objective evolutionary algorithm. One optimal solution was interactively selected according to the preferences of local residents surveyed using a pairwise comparison questionnaire. The proposed method was demonstrated to successfully derive an optimal renewable energy mix for Takashima using backcasting. In addition, it was shown to be a useful method for the co-creation of local energy systems. © 2019 Published by Elsevier Ltd.

Keywords: Local energy system Co-creative design Renewable energy mix Multi-objective optimization Participatory Backcasting

1. Introduction 1.1. Background Backcasting has been considered the best method for realizing the transformation of technology and society toward sustainability [1]. It involves designing a desired, sustainable future or normative scenarios and planning backward to the present to determine how this desired future can be achieved as well as developing necessary

* Corresponding author. Rm#: 609, United Nations University Institute for the Advanced Study of Sustainability, 5-53-70 Jingumae, Shibuya, Tokyo, 150-8925, Japan. E-mail address: [email protected] (K. Hori).

strategies and activities [2]. Backcasting is particularly important for designing sustainable energy systems with renewable energy sources because future energy demand is generally the result of current policy decisions [2] and plans for renewable energy utilization must be compatible with regional characteristics and associated long term social and environmental impacts. In Japan, as rapid population decline [3] and changing industrial structures are expected to result in fundamental societal and economic changes in the near future, backcasting is a useful tool in developing future local energy plans suiting regional future visions. A “participatory backcasting” approach has also been suggested when future scenarios and plans involve diverse stakeholders [2]. Since the 1990s, several participatory backcasting methods have been developed to ensure an interactive and iterative process,

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allowing for continuous feedback regarding design methods for the development of future scenarios. The lack of a participatory process can lead to adverse environmental impacts and limited community acceptance, as demonstrated in several Japanese renewable energy projects [4]. However, regional renewable energy plans in Japan are often not based on quantitative energy demand assessments or participatory backcasting approaches. As a result, renewable energy installations tend to be implemented in an ad hoc manner according to Japan’s feed-in tariff policy [5]. The New Energy and Industrial Technology Development Organization of Japan (NEDO) undertook a project designed to help municipalities formulate new local energy visions [6] and the Regional New Energy and Energy Saving Vision Development Guidebook [55] was developed for the formulation and development of local energy visions on the basis of current energy usage and potential regional renewable energy supply. However, when describing future energy demand, the guide states that “energy demand is predicted based on current trends while paying attention to the municipal vision.” Therefore, many “new regional energy visions” have only been formulated in reference to national renewable energy use targets or potential local supply. The Fifth Basic Environment Plan [7] states that renewable energy is an essential local resource for the formation of a “Regional Circular and Ecological Sphere” and for achieving Sustainable Development Goals locally. “Sustainable Community Development Using Local Resources” has renewable energy use as a priority strategy [52]. Given these policies and plans, in this paper we propose a method to support the establishment of sustainable and consistent local energy systems based on a future vision of holistic sustainable community development. As part of its requirements for desirable renewable energy use, the key foci of the Fifth Basic Environment Plan are local energy balance, local resilience in times of disaster, and industrial competitiveness [7]. Therefore, to determine realistic integrated solutions that both consider the energy balance and address multidimensional societal impacts, a methodological scientific energy system engineering framework, such as mathematical optimization, is essential [8]. Regarding construction of alternative sustainable energy systems in line with future visions, integrated approaches that combine holistic and systematic mathematical models with participatory backcasting must be used. 1.2. Review A number of scholars argue for a participatory approach when planning local energy systems. Belmonte et al. [9] argued that the complexity of technological systems, such as renewable energy, requires holistic analyses. They outlined four prioritized management approaches: institutional strengthening, participative processes, initiative and local development, and integral sociotechnical dynamics. Capaccioli et al. [10] noted the importance of empowering and engaging local citizens and using the concept of “energy justice” to promote both more ethical production and energy consumption. Meanwhile, using a narrative approach to participatory scenario development, Upham et al. [11] applied a structural narrative analysis to 46 energy-emission reduction scenarios developed for 14 European cities. Each employed backcasting to narratively develop future scenarios for 2050, after which the future assumptions were converted into input parameters for an energy-emission dashboard tool [12] to simulate reductions in energy emissions. Similarly, Kowalski et al. [13] and Marinakis et al. [14] employed dynamic backcasting processes to develop participatory designs for local energy systems and use. Kowalski et al. [13] applied and analyzed a process of democratic decision-making on a local energy system in Austria via a combination of scenario development and

participatory multi-criteria analysis termed PROMETHEE II [15]. They then constructed and applied the process over four stages: (1) four sustainable energy scenarios for 2020 were developed to consider the projected future energy supply and demand by stakeholders working in regional energy supply; (2) impacts of each scenario were assessed using various criteria; (3) stakeholders’ individual and group preferences were elicited; and (4) scenarios were ranked using a multi-criteria aggregation method based on stakeholder preferences. Marinakis et al. [14] proposed a participatory multi-criteria decision-making method for the establishment of sustainable energy action plans at a local scale with the scope of the action plan to meet the 2020 CO2 emission reduction goals [16]. They integrated stakeholder views into 38 alternative scenarios and used an extreme ranking analysis method  ski et al. [17] to select an optimal action plan. proposed by Kadzin In these cases, participatory backcasting methods for designing future local energy systems consist of (1) scenario developments that consider future energy supply and demand balance and evaluate anticipated greenhouse gas reductions and (2) multiple criteria analysis for optimal scenario selection involving local stakeholders. However, these studies have focused only on changes in the energy demand and potential alternative behaviors. No energy systems based on future demand and holistic local future visions of desired future society were designed. In addition, none of the studies employed mathematical optimization techniques to construct alternative sustainable energy use scenarios and action plans. Instead, these were manually developed by combining alternative energy options or behaviors. However, determining optimal renewable energy systems involves complex decisionmaking processes that require the application of a scientific mathematical optimization method. Significant research studies have been conducted regarding the construction and use of mathematical optimization in energy system designs [8]. In particular, Wang et al. [18]; Nasiraghdam et al. [19]; and Hori et al. [20,21] sought to optimize local hybrid energy systems by evaluating multiple impacts including environmental impacts. To optimize the combination of wind turbine generators, photovoltaic panels, and storage batteries considering costs, reliability (quality of load supply), and pollutant emissions, Wang et al. [18] employed multi-objective optimization using a metaheuristic algorithm termed Particle Swarm Optimization [22]. Nasiraghdam et al. [19] had four objectives when optimizing a reconfiguration of a distribution system and hybrid (photovoltaic/wind turbine/fuel cell) energy system: total power loss, total electrical energy cost, total pollutant gas emissions, and voltage stability index. They applied a Multi-objective Artificial Bee Colony algorithm [23] to derive the optimal discrete switching element combination in the distribution system. Hori et al. [20,21] developed a Renewable Energy Regional Optimization Utility Tool for Environmental Sustainability (REROUTES) as a multi-objective optimization model for a municipal renewable energy mix that could optimize a renewable energy combination of solar, wind, small- and medium-scale hydro, geothermal, and biomass energy. Using a multi-objective evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II; [24], the renewable energy mix was optimized for six objective functions as follows: renewable energy sufficiency ratio, economic balance, CO2 reduction, ecological impact, biomass recycling ratio, and renewable energy diversity index. 1.3. Purpose of this study In this research, we applied and evaluated a co-creative design support method for local energy systems that consists of (1) participatory development of a local future vision, (2) quantitative projection of future energy demand coupled with the future vision,

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(3) multi-objective optimization of a renewable energy mix based on the projected future demand, and (4) interactive selection of an optimal solution based on stakeholder preferences. The ideas to utilize mathematical tools to project future energy demand from a holistic local future vision and design an optimized renewable energy mix for participatory and backcasting decision-making are the originalities of this research. This is a multi-disciplinary research approach addressing decision-making for utilizing energy generation in a renewable manner at local scale with the integrated approach including engineering tools, socio-economical evaluations, and participatory processes. Notably, “co-creation” includes “collaboration between the scientific community and stakeholders” and “collaborative bottom-up decision-making by stakeholders.” The former is an intention to link scientific knowledge and stakeholder participation in social decision-making [25] such as future demand projections and multiobjective optimization. The later aims to realize the participatory and inclusive development of a future vision and interactive selection of preferences. 2. Materials and methods 2.1. Study site: Takashima, Shiga Prefecture Takashima, Shiga Prefecture, is on the western side of Lake Biwa, Japan’s largest freshwater lake (Fig. 1). It emerged as a city in 2005 following the consolidation of five towns (Makino, Imazu, Adogawa, Takashima, and Shinasahi) and one village (Kutsuki) (Takashima City) [56]. It has a population of approximately 50,000. However, it is expected to undergo a severe population decline and aging over the coming decades [26]. The city has a total area of 693 km2, an annual precipitation of 2,175.5 mm, and a mean annual temperature of 14.7
C. Land cover consists of 53% forest, 26% Lake Biwa, and 8% agricultural land. The primary industrial sectors are agriculture on the plains, fishing on Lake Biwa and in rivers, and forestry [27]. This city was chosen as a Japanese heritage site because of its beautiful lake and riparian landscape and its unique fermented food culture. In 2008, Takashima launched a new regional energy vision [28]. As shown in Fig. 2, the city has several potential renewable energy

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Fig. 2. Takashima’s potential renewable energy supply (data from the REROUTES database).

sources, such as solar, solar thermal, onshore wind, hydro, and biomass from a range of biomass sources. This potential supply was identified from a REROUTES [20] database developed using the “Study of the Potential for the Introduction of Renewable Energy” [29] data and “Estimation of Abundance and Available Amount of Biomass” [30] data. Takashima is suitable for trial application of the proposed method including optimization of the renewable energy mix because of this variety of renewable energy sources. However, harnessing this renewable energy potential has not yet been realized. While the 2nd Takashima Comprehensive Plan, formulated in 2017 [26], mentions the introduction of renewable energy as a policy target for 2026, concrete action plans for achieving this target have yet to established. Thus, application of the developed method to Takashima is a potential contribution to decisionmaking for formulating the renewable energy introduction action plan. Because of this possibility and the aforementioned variety of potential renewable energy sources, Takashima was selected as a case study. 2.2. Case study process Fig. 3 provides an overview of the case study consisting of five processes. First, participatory workshops, facilitated by researchers, were organized to understand regional problems and develop a desirable future vision and solutions for Takashima. Second, following stakeholder establishment of the future vision, Takashima’s future industrial activities and energy demands for 2040 were quantitatively projected using the Extended Snapshot Tool (ExSS), an inputeoutput model. Third, REROUTES model was applied to multi-objective optimization based on the projected future energy demand and potential candidates for the optimal renewable energy mix. Fourth, from the optimal solution candidates, one optimal solution was interactively selected according to the stakeholders’ renewable energy preferences and the significance of the objectives identified through a questionnaire survey completed by a small group of citizens from Takashima. Finally, the utility of the approach was validated in an interview with Takashima City officers. Each specific process is detailed in the following subsections.

Fig. 1. Location of Takashima and Lake Biwa.

2.2.1. Backcasting future vision development in participatory workshops A backcasting approach was applied in the workshops that were conducted in collaboration with the second term of the Takashima Community Promotion Council organized by the Takashima Civic

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Fig. 3. Takashima case study overview.

Cooperation Division. This council, which is composed of 26 citizens and 19 municipal officers, was established in 2015 to develop solutions for regional problems and determine future regional developments through civic consultation and cooperation [31]. The target for fiscal year 2017e2018 was to establish a future vision and investigate the feasibility of citizen-driven planning to realize this vision. The future vision development workshop was held during August 2017 and was attended by nine municipal officers, 14 citizens from the Takashima Community Promotion Council, and two non-council citizens. First, workshop participants were informed of the projected near future events in Takashima, such as “a decrease in the number of local small shops” and “an increase in vacant houses.” Participants discussed these events in line with the population decline and global warming trends in a “Business as Usual” scenario. Hopes for the future of Takashima with a time horizon to 2040 were first discussed and the necessary actions required to realize these hopes were argued. To provide guidance for the discussion, participants were presented with four questions related to demographic dynamics, industry, and lifestyle in Takashima as follows: (1) What type of work do you want to do? (2) How do you want to spend your free time? (3) What do you want to retain in Takashima? (4) What do you want to spend money on? 2.2.2. Future energy demand projections using ExSS Qualitative future vision data regarding potential future economic activities and potential lifestyle changes in 2040 were then converted into quantitative data and fed into the ExSS mathematical model to project Takashima’s plausible future energy demands. ExSS, a type of inputeoutput model, was developed for quantitatively projecting a plausible future vision and exploring the necessary actions and pathways to achieve it; it has been applied by Shimada et al. [32]; Gomi et al. [33,34], the Shiga Prefecture Sustainable Society Research Team [35]; Yura et al. [36]; Naito [54]; and Kim et al. [53]. ExSS can express the relationships between “social and economic assumptions,” “activities in residential, industrial, and transport sectors,” and “energy consumption and CO2 emissions associated with these activities” in a target area using only one mathematical model. Given the ideal social and economic system settings, ExSS can comprehensively estimate the environmental burden on the basis of an inputeoutput model that includes an energy balance table [32].

The ExSS structure is shown in Fig. 4. As the basis for future estimations, ExSS requires data on the regional industrial structure (e.g., dominant industries that export regional goods and services, industries strongly dependent on outside supplies, and labor productivity) and regional population demographics (e.g., age distribution, employment rate, employment type, household expenditure, and standard household composition) as input parameters. Based on these parameters, socioenvironmental states were calculated as solutions to simultaneous equations to balance the residential labor force needed to establish the industry and scale of industry necessary to satisfy residential consumption. Based on the determined industrial structure and population, energy-consuming activities in the residential (residential/commercial), industrial, and transport sectors (passenger/freight) were then projected as “services,” such as air conditioning in the residential sector or passenger travel in the transport sector. Energy consumption in each sector was calculated according to the diffusion rate and efficiency of equipment used, after which future CO2 emissions were estimated by multiplying the coefficient. Shiga Prefecture was divided into eight regions in ExSS, one of which was Takashima. As baseline data, the parameters were validated to enable replication of the past energy consumption values and macro-economic indicators in 2000. The primary parameters were collected from the proofed statistical data and estimated parameters in the model were also verified by ensuring that the values were consistent with the range of past fluctuations. For 2040, the qualitative industrial structure and lifestyle future vision descriptions for Takashima that had emerged from the participatory workshops were converted into quantitative parameters and the future energy consumption in each industrial sector was calculated. Some future parameters such as diffusion rates and equipment efficiency data were input with reference to the target values of Japan’s Global Warming Countermeasure Plan [37]. 2.2.3. Calculation of Pareto optimal solutions for a renewable energy mix using the multi-objective optimization model REROUTES Based on future energy demand, as projected by ExSS, candidates for an optimal renewable energy mix that would realize the future vision was derived using the multi-objective optimization model REROUTES. This model uses six evaluation indicators to calculate the optimal Japanese municipal renewable energy mix [20,21,38] and provides potential superior renewable energy mix

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Fig. 4. ExSS overview.

solutions, under the energy demand data and potential supply constraints, and evaluates environmental and economic impacts. Solar, wind, small- and medium-scale hydro, geothermal, and biomass energy were all considered as potential forms of renewable energy. In the latest REROUTES version, the variables are the amount of renewable energy developed from 267 renewable energy types (TJ/year) with six renewable energy mix evaluation indicators as follows: renewable energy sufficiency ratio, economic balance, CO2 reduction, ecological impact, biomass recycling ratio, and renewable energy diversity index. The parameters used for calculation of evaluation indicators were verified by past observed

data and actual performance data of real renewable energy systems [20,21,38]. The REROUTES database contains information regarding the potential supply from renewable energy sources and energy demand by sector for all 1,742 Japanese municipalities. REROUTES can also help conduct single-objective and multi-objective optimization. In the multi-objective optimization option, a visualization of the tradeoff relationships between evaluation indicators is executed using a genetic algorithm, NSGA-II [24]. The result is an output of “Pareto optimal solutions,” indicating that none of the objective functions can be improved without degrading the other

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objective values. Finally, REROUTES provide a set of optimal renewable energy mix candidate solutions with tradeoff relationships and expected values for the six evaluation indicators. Details of the multi-objective version of REROUTES are shown in Fig. 5; the applied optimization algorithm is provided in the Supplement. In the case study, the future energy demand for 2040, as projected by ExSS, was input and used as a regional energy demand constraint. For the optimization process, the multi-objective version of REROUTES was employed to calculate the potential renewable energy mix solutions. Multi-objective optimization calculation was completed under the conditions outlined in Table 1. The calculated solution candidates were clustered into six groups by the compositions of the mixed renewable energy sources to grasp the compositions and the distribution of values in the evaluation indicators of the Pareto optimal solutions. The tradeoffs between the solutions and six evaluation indicators were visualized in a scatter plot matrix after clustering. A self-organizing map [39] was applied to the clustering algorithm and the number of clusters was decided by determining the elbow point within the sum of the squared criteria. The centroids for renewable energy composition and evaluation indicators for each cluster were then calculated as representative features to attain total Pareto optimal solutions.

Table 1 Optimization conditions.

Variables Constraints Objective functions Individuals Iterations

Number

Parameter

Value

267 174 6 10,000 1,000

nc ta tn nm Pm

2 10 500 3 0.0001

2.2.4. Co-creative selection of the optimal solution using a preference survey During the next stage of the process, stakeholders co-creatively selected the optimal solution from the list of renewable energy mix solution candidates. This stage showed on a trial basis whether the selection process considering preferences of multiple citizens could successfully function. As the first step, a questionnaire survey was conducted for three Takashima citizen stakeholders who were involved in renewable energy implementation activities. The questionnaire conducted a pairwise comparison of the importance of the evaluation indicators and renewable energy type priorities. The three respondents were asked to prioritize the composition of

Fig. 5. REROUTES overview.

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each renewable energy type and the evaluation indicators before selecting one specific optimal solution from the Pareto optimal solutions. The questionnaire results were then quantified and the optimal renewable energy mix was interactively selected according to these results. The three respondents were later interviewed to ascertain their reasons for prioritizing certain renewable energy types and evaluation indicators. Based on the questionnaire results, relative priorities for the renewable energy mix and evaluation indicators were obtained. By repeatedly comparing the distances between the quantified stakeholder preferences and centroids for each renewable energy mix cluster, an optimal solution was determined. 2.2.5. Utility evaluation To evaluate the utility and challenges of the co-creative design support method from a practical decision-making perspective, an interview was conducted during December 2017 with four Takashima City officers who address projects related to renewable energy and civic cooperation. Using a semi-structured interview format, interviewees were asked the following questions: (1) Is the proposed method useful for your municipality’s decision-making on renewable energy introduction? (2) Why do you think the proposed method will be useful or not as answered? and (3) Are there any suggested improvements to enhance the utility of the method for practical decision-making? 3. Results and discussion 3.1. Backcasting future vision development in participatory workshops Because of space limitations, only a summary of the described future vision is outlined here. Given the current trajectory, it is expected that a population decline and an aging society will continue until 2040. Therefore, within the context of depopulation, the desired future vision for Takashima is a city in which citizens can realize their well-being through maximum utilization of local resources, including natural and human resources, traditional culture, and connections between citizens [40]. In the proposed future vision, materials supplied by the forestry industry and the region’s abundant forest resources will be used in local industries to generate employment. The picturesque natural landscape will be well managed and maintained to encourage tourism. Traditional cuisine based on locally produced vegetables and fish from Lake Biwa and local rivers will be promoted. Diverse workplaces will utilize regional resources and allow citizens to flexibly work in agriculture, local restaurants that use local food, delivery services for the elderly, and sales of local products through the Internet and as sightseeing guides promoting the nature, history, and culture of Takashima. Citizens will be able to live freely and easily, raising children, caring for the elderly with the support of their local communities, and enjoying hobbies and community activities during their free time. Citizens will deepen their relationships by learning from each other to improve their local knowledge regarding local industries, traditions, and culture and Takashima’s biodiversity. This practical local education, provided by citizen teachers in local schools, will imbue community pride and develop future human resources to support local communities. Young citizens and migrants will be integrated into the community through local salons, events, and festivals, and elderly people will be encouraged to participate through the development of mobile support services. Community services will be established to support the safety and health of all citizens. This vision describes a renewable energy future that will restrict the installation of solar panels in fields and develop woody biomass energy.

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3.2. Future energy demand projections using ExSS Based on this future vision, social, economic, and environmental conditions were quantitatively parameterized and projected using ExSS. Table 2 shows the input parameters and outputs from ExSS. Fig. 6 shows the future energy demand in Takashima as projected by ExSS. The numerical values in Table 2 reflect the qualitative future vision outlined in the previous section. For example, the working population ratio within Takashima is expected to increase by 5% from 2000 to 2040; the time spent on social activities is expected to increase; the working hours for employed men are expected to decrease by 2 h per day; new community works such as community buses or child care programs will be implemented; and labor productivity is expected to improve by 47% in agriculture, 26% in manufacturing, and 30% in services. Public expenditure on transportation and medical care is expected to be maintained to continue administrative support for citizens; however, overall a reduction in public expenditure is expected. Using 2000 as the baseline, the energy demand is projected to decrease by approximately 25% with approximately 50% of the energy demand in 2040 originating from the industrial sector and largely from manufacturing, 20% from the residential and transport sectors, and 10% from the commercial sector. A decrease in demand from the residential sector will result from increased energyconservation efforts through active community interchange or sharing and installation of household energy-efficient technology. This improvement in energy-efficient technology will also contribute to decreased energy demand in the commercial and transportation sectors. It is also expected that, because of the enhanced work in Takashima and local consumption, the transport of goods both in and out of Shiga Prefecture will decreases, further reducing energy demand in the transportation sector. From the estimated current potential supply, shown in Fig. 2, and the projected demand balance in 2040, shown in Fig. 6, it is evident that the potential renewable energy supply will exceed the projected future energy demand. The projected energy demand shown in Fig. 6 was used as the input data of REROUTES for optimization of the renewable energy mix. 3.3. Calculation of Pareto optimal solutions for a renewable energy mix using the multi-objective optimization model REROUTES REROUTES was applied to calculate the Pareto optimal solutions by referring to the 2040 energy demand output by ExSS. Pareto optimal solutions in the scatter plot matrix of evaluation indicators are shown in Fig. 7. Ten thousand optimal solutions of the renewable energy mix are divided into six clusters by the composition of the renewable energy mix and plotted using six different colors. The centroids for the amounts of developed renewable energy and values for the evaluation indicators in each cluster are shown in Fig. 8. The features of each cluster of solutions can be understood reviewing these figures. For example, the optimal solutions in Cluster 1 (in red, Fig. 7) indicate that wind power, hydropower, and solar power should be installed rather than solar thermal power (the left bar in Fig. 8). This composition of renewable energies made Cluster 1 a group of economically oriented solutions compared to that of the other clusters; it was the cluster plotted at the top of the economic balance axis in the upper left graph of Fig. 7 and the centroided value of the indicator was the highest in Fig. 8. However, the renewable energy diversity index in Cluster 1 was the lowest because of the large differences between wind power and other energy sources, such as solar thermal power; it was the cluster plotted in the lowest range of the renewable energy diversity index axis in the bottom graphs of Fig. 7. In this manner, from the distribution of values for

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Table 2 Parameters and output for ExSS, reflecting the narrative future vision (partly extracted)*.

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*

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approximately 110 Japanese yen ¼ 1 US dollar [41].

each evaluation indicator colored by cluster, the tradeoffs and synergies of each cluster of solutions can be visualized. The centroided values of the renewable energy mix and evaluation indicators enabled understanding and analysis of the factors determining the values of the evaluation indicators and the tradeoffs and synergies. As biomass resources, as sources of biomass energy, were evaluated to have lower a potential than that of other energy resources, it was necessary to use as much biomass energy as possible to obtain a high diversity index value. Cluster 6 (in purple, Fig. 7) shows high renewable energy diversity, in which both the biomass recycling ratio and the renewable energy diversity index were the highest compared to the other clusters. However, the renewable energy sufficiency ratio and CO2 reduction were relatively low. The ecosystem impact was high in Clusters 4 and 5 (in light blue and blue, respectively, in Fig. 7) because the solutions in these clusters involved the development of distributed resource herbaceous biomass, such as Sasa and Miscanthus (as shown in the bars in Fig. 8), resulting in a larger area of impacted ecosystem. In this manner, calculation and visualization of Pareto optimal renewable energy mix solutions enabled determination of the various feasible renewable energy mixes based on local energy sources and the impacts and tradeoffs of these from aspects of multiple evaluation indicators.

3.4. Co-creative selection of the optimal solution using a preference survey Based on the pairwise comparison results derived from the questionnaire completed by the three Takashima citizens, the

Fig. 6. Future energy demand output by ExSS (2040) and the observed energy demand in 2000.

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Fig. 7. Scatterplot matrix showing the distribution of clustered Pareto optimal solutions*. * approximately 110 Japanese yen ¼ 1 US dollar.

Fig. 8. Centroid for the renewable energy mix and evaluation indicator values by cluster* (superior values in the same evaluation indicator are shown in a darker red color).. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Table 4 Relative priorities of renewable energy types.

Table 3 Relative importance of the evaluation indicators. Evaluation indicator

Relative importance

Renewable energy type

Relative priority

Renewable energy sufficiency ratio Economic balance CO2 reduction Ecological impact Biomass recycling ratio Renewable energy diversity index

0.15 0.07 0.20 0.21 0.13 0.23

Solar power Solar thermal power Onshore wind power Hydropower Geothermal power Woody biomass Waste biomass Residuary biomass

0.08 0.09 0.07 0.36 0.03 0.23 0.09 0.05

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Fig. 9. Left: comparison of indexed values for each cluster and relative significance of the evaluation indicators. Right: comparison of the indexed amount of developed renewable energy for each cluster and relative priority of renewable energy types.

quantified relative priority for renewable energy types and relative importance of the evaluation indicators are shown in Tables 3 and 4, respectively. For renewable energy types, hydropower and woody biomass energy were preferred. Respondents commented that the installation of renewable energy was not the direct purpose and that they aimed to provide various positive influences on Takashima via renewable energy, such as revitalization of the region via a small hydropower installation and mitigation of global warming via utilization of woody biomass energy from local forests as a carbon-neutral energy source. Although hydropower was prioritized, the interviews also showed that woody biomass is the preferred energy source; however, hydropower is the most economically advantageous. It should be noted also that some respondents admitted to interpreting “priority” to mean “priority to promote installations that need large-scale facilities and are difficult for individual citizens.” For the evaluation indicators, the renewable energy diversity index was the most desired indicator, followed by ecosystem impacts and CO2 reduction. The renewable energy diversity index was important because it was believed that complementary relationships between multiple renewable energy types were significant. The promotion of woody biomass, for example, has a low economic advantage and would need to be financially subsidized by hydropower, which is economically superior. Ecological impacts and CO2 reduction were considered important because of climate change concerns and the potential for increased typhoon and flood threats in Takashima. However, it was also believed that local natural ecosystems should not be sacrificed to develop renewable energy, even if this was to mitigate climate change effects. Using the results of these preference surveys and the calculated Pareto optimal solutions, optimal renewable energy mix solutions for Takashima were refined on the basis of a co-creative selection approach. The radar charts in Fig. 9 show evaluation indicators and renewable energy mix features by cluster, including the standardized centroid with relative importance and relative priority. When the radar chart cluster shape is similar to the relative importance and relative priority, as indicated by the red line, the cluster is nearer acceptable stakeholder preferences. Cluster 4 had the highest weighted score calculated by the sum of the products of both the centroid of the evaluation indicators and relative importance and the renewable energy mix and relative priority. Cluster 4 had a superior renewable energy diversity index, CO2 reduction, hydropower, and woody biomass use. However, it was at a

disadvantage, compared to other clusters, as it had ecosystem impact as the second most important indicator. Therefore, solutions with the least ecological impact in Cluster 4 were selected as candidates for the optimal solution. Fig. 10 shows the five solutions with the lowest ecosystem impact in Cluster 4. Solutions AeE are in an ascending order of ecosystem impact. The five solutions were compared to each other and Solution A was selected as the optimal renewable energy mix solution for Takashima because of its superior renewable energy diversity index, ecosystem impact, CO2 reduction, and hydropower and woody biomass combination. 3.5. Utility evaluation1 3.5.1. Overall result of utility evaluation All four interviewees affirmed that the proposed method was useful for local renewable energy use decision-making. In particular, the reliability that an optimal renewable energy mix would be proposed on the basis of expert knowledge and sophisticated calculations was positively evaluated. As it is often difficult for city officials to execute calculation processes, this approach could enhance the scienceepolicy interface [25]. In addition, the method was also evaluated as having high citizen utility, as the optimization was positioned as a step within the decision-making process based on local opinion regarding the potential renewable energy supply. The visualized tradeoff relationship helped deepen the understanding of the process of local stakeholder consultation. It has been requested that, with a larger sample of citizens via a social survey planned by the Takashima City Environmental Policy Division, the proposed method be used for practical policy making. 3.5.2. Suggestions for further improvement To make the proposed method more effective for the formulation of actual action plans, four improvements are suggested. First, evaluation indicators should be adapted to determine the downscaled effects at the community level to support decisionmaking for local community problem-solving. Takashima has more than 200 communities and it was noted that if the citizens were more aware of the merits or possible contributions of

1 By reviewing and recalculating the data, the presented results at the interview for utility evaluation were found to be different from the results in this report

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Fig. 10. Five solutions with the lowest ecological impact in Cluster 4 * approximately 110 Japanese yen ¼ 1 US dollar. (superior values in the same evaluation indicator are shown in a darker red color)*.. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

renewable energy in solving local problems, such as the increasing number of vacant houses or the damage caused by harmful wildlife, and if these issues were made relevant at a community scale, greater community consensus would follow. An example of the possible contribution of renewable energy use to local issues is the Itoshiro community in Gujo, Gifu Prefecture. The promotion of a small-scale hydropower project [42] in that community increased the number of visitors and encouraged in-migration, mitigating the local population decline. Therefore, in addition to more efficient energy use, as required by the Regional Circular and Ecological Sphere [7], the selection of appropriate renewable energy can contribute to a local community’s desirable future vision. This reflects the concept of “multilevel governance” [43], whereby effects should be evident at both the municipal and community levels. Second, the focus on sharing renewable energy options and calculations should be improved. Although the potential supply of renewable energy can be expressed as a numerical value, many citizens are unfamiliar with the different types of renewable energy and, therefore, have difficulty judging their merits and demerits. Moreover, when practically formulating the action plan, opinions regarding renewable energy preferences must be obtained from a more diverse range of citizens, including those unfamiliar with energy issues or energy systems. If this is to be completed, basic information regarding options and renewable energy calculations must be made easier to understand by non-experts. One suggestion is to share with local stakeholder’s detailed information regarding the characteristics, benefits, and conditions for utilizing renewable energy prior to the preference survey. When the Japanese government encouraged a national debate on a national energy mix after the Great East Japan Earthquake, easily accessible information regarding energy issues was provided to the general public [44]. A database on the national energy issue and three future energy options were published on the Internet and two different brochures prepared for children and others included many easy-tounderstand illustrations [45]. Visualization methods utilizing workshops and artistic media for sharing of scientific information have been evaluated and developed as public decision-making tools regarding various scientific issues (JST Center for Science

Communication). Therefore, a method needs to be developed to explain the benefits of renewable energy implementation to residents. Third, it is essential to have expert participation during the optimal solution selection process. It was recommended by interviewees that experts from energy research fields be included during the optimal solution selection process. Even though Pareto optimal solutions were calculated using scientific methods, it is possible that an optimal solution could contain risks that cannot be recognized without expert advice. As Mikami et al. [46] argued, it has been widely recognized that the participation of citizens in decision-making is necessary; however, this does not exclude experts from social decision-making. Rather, it indicates the importance of adjusting the relationship between expert judgment and citizen opinion during the participatory process. Therefore, a framework should be developed to reflect expert knowledge within a Consensus Conference [47] and Citizens’ Jury [48] such that citizens can have a discussion with and question experts before deciding. Fourth, dynamic projection of future potential renewable energy supply must be improved. In this research, while energy demand was estimated using a backcasting method, the potential supply was set at the same level as stated in the 2010 REROUTES municipal energy database [20]. However, the potential renewable energy supply is expected to change in reaction to future changes in urban structure or climate. For example, the future potential supply of solar power and solar thermal systems could be projected by simulating the transition of available space, such as roofs, where facilities can be installed. Tsujita [49]; in an analysis of the factors affecting urban solar power installations, constructed a model to project future potential supply and the spatial distribution of solar power under future urban structural changes. Regarding biomass energy, Pambudi et al. [50] argued that, as a practical approach to increasing future biomass energy needs, the potential supply of biomass energy could and should be increased by importing and cultivating new energy crops in Japan. The potential supply of such energy crops could be simulated using biogeochemical models for biomass production. By combining such sub-models, the proposed

Please cite this article as: K. Hori et al., Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process, Renewable Energy, https://doi.org/10.1016/j.renene.2019.11.089

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method in this study could be expanded to optimize the renewable energy mix based on future potential supplies. 4. Conclusion and future directions In this study, a co-creative local energy system design support method with participatory development for a local future vision was proposed that employed multi-objective optimization to determine a regional renewable energy mix consistent with a local future vision. A co-creative optimization process that reflected the preferences of local residents was also proposed. A case study in Takashima, Shiga Prefecture, Japan, demonstrated that the proposed method can successfully derive an optimal future renewable energy plan based on scientific methods, such as mathematical simulation and optimization, and local stakeholder opinions regarding a local future vision and energy system. Policymakers in the case study area claimed that the proposed method was useful for co-creative local energy system design and was practically useful for the formulation of a renewable energy use action plan. This study integrated several tools and methods such as participatory backcasting and multi-objective optimization which have been separately developed and applied. This research contributed to developing a multi-disciplinary method to link the design of renewable energy implementation and local future vision by utilizing a scientifically evidenced and participatory approach. The utility of the integrated method was shown by trial application in a case study area utilizing an available dataset. It showed that a series of proposed processes could function to achieve one renewable energy mix in backcasting manner starting from a desirable local future vision developed by local residents through scientific projection, optimization, and evaluation. Under the current situation that both a consensus in local society and necessary technical knowledge were essential to introduce renewable energies [51], this study can provide a meaningful crosscutting approach considering the perspective of practical use. Future work will focus on an extension of the proposed cocreative design support method related to utility evaluation findings. Specifically, the planned improvements include (1) advancing the spatial resolution of the evaluation of impacts of the renewable energy mix to identify ripple effects at the community level, (2) integrating visualization methods for sharing of renewable energy information, (3) adding a process that includes expert participation in the selection of the optimal solution in an advanced co-creative optimization process, and (4) combining sub-models to project future potential renewable energy supplies. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was conducted with the cooperation of Takashima City officers and citizens and support by Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for JSPS Fellows (16J00845) and Grant-in-Aid for JSPS Scientific Research (T16K006510, 17K00707, 19K20494). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.renene.2019.11.089.

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References [1] Philip J. Vergragt, Jaco Quist, Backcasting for sustainability: introduction to the special issue, Technol. Forecast. Soc. Chang. 78 (5) (2011) 747e755. [2] Jaco Quist, Philip Vergragt, Past and future of backcasting: the shift to stakeholder participation and a proposal for a methodological framework, Futures 38 (9) (2006) 1027e1045. [3] National Institute of Population and Social Security Research, Population projection for Japan by municipalities 2015-2045. http://www.ipss.go.jp/ppshicyoson/j/shicyoson18/t-page.asp, 2018. (Accessed 31 January 2019). [4] Institute for Sustainable Energy Policies, Renewables Japan status report 2017. http://www.isep.or.jp/jsr/2017report, 2018. (Accessed 31 January 2019). [5] Agency for Natural Resources and Energy, Official homepage of feed-in tariff. http://www.enecho.meti.go.jp/category/saving_and_new/saiene/kaitori/ index.html. (Accessed 11 March 2019). [6] NEDO, The implementation policy for FY 2010 of regional new energy and energy saving vision development project. https://www.nedo.go.jp/content/ 100084843.pdf, 2010. (Accessed 26 February 2019). [7] Ministry of the Environment, The Fifth basic environment plan. https://www. env.go.jp/en/policy/plan/5th_basic/outline_14.pdf, 2018. (Accessed 6 March 2019). [8] Aqeel Ahmed Bazmi, Gholamreza Zahedi, Sustainable energy systems: role of optimization modeling techniques in power generation and supplyda review-, Renew. Sustain. Energy Rev. 15 (2011) 3480e3500. [9] Silvina Belmonte, Karina Natalia Escalante, Judith Franco, Shaping changes through participatory processes: local development and renewable energy in rural habitats, Renew. Sustain. Energy Rev. 45 (2015) 278e289. [10] Andrea Capaccioli, Giacomo Poderi, Mela Bettega, Vincenzo D’Andrea, Exploring participatory energy budgeting as a policy instrument to foster energy justice, Energy Policy 107 (2017) 621e630. [11] Paul Upham, Rita Klapper, Sebastian Carney, Participatory energy scenario development as dramatic scripting: a structural narrative analysis, Technol. Forecast. Soc. Chang. 103 (2016) 47e56. [12] S. Carney, S. Shackley, The greenhouse gas regional inventory project GRIP): designing and employing a regional greenhouse gas measurement tool for stakeholder use, Energy Policy 37 (2009) 4293e4302. [13] Katharina Kowalski, Sigrid Stagl, Reinhard Madlener, Ines Omann, Sustainable energy futures: methodological challenges in combining scenarios and participatory multi-criteria analysis, Eur. J. Oper. Res. 197 (2009) 1063e1074. [14] Vangelis Marinakis, Haris Doukas, Panos Xidonas, Constantin Zopounidis, Multicriteria Decision Support in Local Energy Planning: an Evaluation of Alternative Scenarios for the Sustainable Energy Action Plan, Omega, vol. 69, 2017, pp. 1e16. [15] J. Figueira, S. Greco, M. Ehrgott, Multiple Criteria Decision Analysis State of the Art Surveys, Springer, Berlin, 2005, p. 1045. [16] EC e European Commission, How to Develop a Sustainable Energy Action Plan (SEAP) e Guidebook. Covenant of Mayors 2010 Brussels, Belgium, 2010.  ski, Salvatore Greco, SŁowin  ski Roman, Extreme ranking [17] MiŁosz Kadzin analysis in robust ordinal regression, Omega 40 (4) (2012) 488e501. [18] Lingfeng Wang, Chanan Singh, Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm, IEEE Transactions On Energy Conversion, vol. 24, 2009, p. 163, 1. [19] H. Nasiraghdam, S. Jadid, Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm, Sol. Energy 86 (2012) 3057e3071. [20] Keiko Hori, Takanori Matsui, Takashi Hasuike, Ken-ichi Fukui, Takashi Machimura, Development and application of the renewable energy regional optimization utility tool for environmental sustainability: REROUTES, Renew. Energy 93 (2016) 548e561. [21] Keiko Hori, Takanori Matsui, Satoshi Ono, Ken-ichi Fukui, Takashi Hasuike, Takashi Machimura, Development and Application of a Multi-Objective Optimization Tool for Renewable Energy Mix in Municipalities vol. 33, Transactions of the Japanese Society for Artificial Intelligence, 2018, 3, FSGAI01_1-11. [22] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. IEEE Proc. Int. Conf. Neural Netw., Perth, Australia, 1995, pp. 1942e1948. [23] B. Akay, D. Karaboga, A modified artificial bee colony algorithm for realparameter optimization, Inf. Sci. 192 (1) (2012) 120e142. [24] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002) 182e197. [25] Future Earth, Future earth initial design, international council for science. http://futureearth.org/sites/default/files/Future-Earth-Design-Report_web. pdf, 2013. (Accessed 1 February 2019). [26] Takashima City, 2nd Takashima City Comprehensive Plan, 2017. [27] Takashima City, 2nd Takashima City Basic Environment Plan, 2017. [28] Takashima City, Takashima City New Energy Vision, 2008. [29] Ministry of the Environment, Study of potential for the introduction of renewable energy. http://www.env.go.jp/earth/report/h23-03/, 2011. (Accessed 7 March 2019). [30] NEDO, Estimation of Abundance and Available Amount of Biomass, 2011. [31] Takashima City, Outline for Establishment of Takashima City, Community Promotion Council, 2015. [32] Koji Shimada, Yoshitaka Tanaka, Kei Gomi, Yuzuru Matsuoka, A method development for long-term local scenario formulation towards a low carbon

Please cite this article as: K. Hori et al., Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process, Renewable Energy, https://doi.org/10.1016/j.renene.2019.11.089

14

[33]

[34]

[35]

[36]

[37]

[38]

[39] [40] [41]

[42]

[43]

[44]

K. Hori et al. / Renewable Energy xxx (xxxx) xxx socity and its pionerring application to Shiga prefecture, Environ. Syst. Res. 34 (2006) 143e154. Kei Gomi, Koji Shimada, Yuzuru Matsuoka, Development of integrated estimation tool for municipalities and its application to Shiga prefecture, Environ. Syst. Res. 35 (2007) 255e264. Kei Gomi, Jaegyu Kim, Yuzuru Matsuoka, A Methodology for Developing a Roadmap Roadmap Local Low-Carbon Society Cosidering Implementation Cost, Environmental Systems Research, vol. 39, 2011 (Selected papers on Environmental Systems Research Vol.67, No.6), pp.II_225-234. Shiga Prefecture Sustainable Society Research Team, Shiga’s scenario towards the realization of a sustainable society in 2030. http://www.pref.shiga.lg.jp/d/ biwako-kankyo/lberi/03yomu/03-01kankoubutsu/03-01-03research_report/ no3/files/18_01.pdf, 2007. (Accessed 13 February 2019). Tomoaki Yura, Kei Gomi, Koji Shimada, Yuzuru Matsuoka, A study of policy options towards a low carbon society considering characteristics of regions, Proc. Ann. Meet. Environ. Syst. Res. 36 (2008) 37e44. Ministry of the Environment, Global warming countermeasure plan. https:// www.env.go.jp/earth/ondanka/keikaku/taisaku.html, 2016. (Accessed 2 February 2019). M. Nishiguchi, K. Hori, T. Matsui, T. Machimura, Optimization of renewable energy mix integrating diverse utilization options of biomass resources, Proc. Conf. Energy Econ. Environ. 34 (2018), 34-3. T. Kohonen, The self-organizing map, in: Proceedings of the IEEE 1990, vol. 78, 1990, pp. 1464e1480. Takashima City, A Report on the Second Term of Takashima City Community Promotion Council, 2019. Takashima City, Comprehensive strategy for overcoming population decline and vitalizing local economy of Takashima city. http://www.city.takashima.lg. jp/www/contents/1446036240774/files/senryaku_291122.pdf, 2017c. (Accessed 13 February 2019). Lei Zha, Kazuki Taketoshi, Roles of small hydropower in solving problems in rural areas: a case study of Itoshiro village and yoshino town, J. Rural Probl. 52 (4) (2016) 247e252. Jens-Phillip Petersen, The application of municipal renewable energy policies at community level in Denmark: a taxonomy of implementation challenges, Sustain. Cities Soc. 38 (2018) 205e218. Energy and Environment Council, Ministry of Economy, How to Proceed National Debate on Options for Energy and the Environmental, 2012. https:// www.cas.go.jp/jp/seisaku/npu/policy09/pdf/20120820/20120820.pdf. (Accessed 8 March 2019).

[45] National Policy Unit, Cabinet secretariat, official website. https://www.cas.go. jp/jp/seisaku/npu/policy09/sentakushi/index.html. (Accessed 8 March 2019). [46] Naoyuki Mikami, The construction of "citizen/expert" in public participation processes, Kobe Law J. 60 (2) (2010) 430e452. [47] Tadashi Kobayashi, Dare ga Kagaku-Gijutsu Ni Tsuite Kangaerunoka: Konsensasu Kaigi Toiu Jikken (Who Should Deliberate on Science and Technology? Consensus Conference as an Experiment), Nagoya University Press, 2004. [48] Kei Sagara, Citizens’ Jury in environmental policy decision process, J. Water Environ. Issues 15 (2002) 31e39. [49] Yurina Tsujita, Spatial analysis of the installments of photovoltaics equipment and evaluations of their potential in case of erlangen of Germany and nishinomiya of Japan, Kwansei Gakuin Policy Stud. Rev. 23 (2017) 17e20. [50] N.A. Pambudi, K. Itaoka, A. Chapman, N.D. Hoa, N. Yamakawa, Biomass energy in Japan: current status and future potential, Int. J. Smart Grid Clean Energy 6 (2) (2017) 119e126. [51] Institute for Sustainable Energy Policies, Renewables 2018/2019 Japan status report (summary). https://www.isep.or.jp/wpdm-package/jsr2018, 2019. (Accessed 13 October 2019). [52] Environmental Strategy Division and Climate Change Policy Division, Ministry of the Environment, Material for Information Liaison Committiee on the Sustainable Introduction of Renewable Energy in the Regions “The Measures towards the Formation of the Regional Circular and Ecological Sphere and the Construction of a Distributed Energy System”, 2019. http://www.enecho.meti. go.jp/category/saving_and_new/saiene/renewable/community/dl/02_02.pdf. (Accessed 6 March 2019). [53] Jaegyu Kim, Takashi Iwakawa, Masaaki Naito, Making a vision and roadmap to realize sustainable society based on numerical quantification of citizens opinion : making a new indicator by utilizing regional information system, Environ. Sci. 28 (1) (2015) 50e62. [54] Masaaki Naito, R&D project final report. "Shiga model: future vision of sustainable society and its realization procedure. http://www.kiess.org/wpcontent/uploads/JST_finalreport.pdf, 2012. (Accessed 7 March 2019). [55] NEDO, Regional New Energy and Energy Saving Vision Development Guidebook, 2003, p. 13. [56] Takashima City : official homepage “Administrative information”. http:// honyaku.j-server.com/LUCSTKSC/ns/tl.cgi/http%3a//www.city.takashima.lg.jp/ www/genre/0000000000000/1133851843633/index.html? SLANG¼ja&TLANG¼en&XMODE¼0&XPARAM¼keyword,&XCHARSET¼UTF8&XPORG¼,&XJSID¼0. (Accessed 1 February 2019).

Please cite this article as: K. Hori et al., Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process, Renewable Energy, https://doi.org/10.1016/j.renene.2019.11.089