Stakeholder-oriented Energy Planning Support in Cities

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Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL doi: 10.1016/j.egypro.2015.11.327

Energy Procedia 78 ( 2015 ) 1841 – 1846

ScienceDirect

6th International Building Physics Conference, IBPC 2015

Stakeholder-oriented energy planning support in cities

Najd Ouhajjou

a,

_{*, Wolfgang Loibl}

a

_{, Stefan Fenz}

b

_{, A Min Tjoa}

b

a_{AIT- Austrian Institute of Technology, Vienna, Austria} b_{Vienna University of Technology, Vienna, Austria}

Abstract

The successful implementation of urban energy planning strategies (applied as a set of measures to improve energy efficiency and carry out distributed renewable energy generation to reduce CO2 emissions) depends on the satisfaction of the stakeholders,

involved in future implementation process. This paper presents a stakeholder-oriented approach, implemented in a planning support system, to provide stakeholders with specific information from their points-of-view, regarding the impact of energy strategies on their interests. The approach is based on semantic web technologies, where an ontology is gradually developed to be used to provide information to different stakeholders during the process of developing urban energy strategies. Measures to be implemented are defined. For each measure, involved stakeholders are identified and questions they raise for their decision making are listed, as competency questions of the ontology. Computation models to answer these questions are identified or developed, based on the data availability in the city. The semantics used in these models are then captured and classified within the ontology. Then the decision making knowledge of the stakeholders is integrated within the ontology, as inference rules. Finally, the ontology is used through a web-map-based interface. The proposed solution anticipates the potential decisions of the different stakeholders, easing the progress of the energy planning process, typically happening in workshops or forums in collaboration with different stakeholders.

Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL. Keywords: urban energy planning; planning support; energy systems modeling; ontologies

* Corresponding author.

E-mail address: [email protected]

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emissions in the world are emitted in cities. Therefore, energy strategies (sets of measures) at the level of cities are required. (ii) Thus, it is essential to assess the impact of these energy strategies, making sure that they do contribute in reaching the desirable objectives, with no unwanted side-effects. (iii) The impact assessment of such city-level strategies requires modeling -relevant parts of- the city, which is challenging due to its size, dynamics, the diversity of domains that are involved, and the plurality of stakeholders with different interests.

There exist numerous definitions of “urban energy planning”. However, the focus of this paper addresses integrated long-term urban energy planning that relies on supporting (software) models [1]. Sub-section 1.1 addresses the definition of urban energy planning processes that are within the scope of this paper. In sub-section 1.2, we give a brief overview of existing supporting tools for the urban energy planning process.

1.1. Urban energy planning support processes

Williams [2] summarizes the primary goal of urban energy planning processes as embedding the decision making proces in a conceptual framework, thus, defining some structure concerning what is needed to be accomplished. Accordingly, more emphasis is put rather on the process and actors than on the content and the structures. A more generic definition of urban energy planning processes, which also fits within the scope of this paper, has been given by Mingers and Brocklesby [3], defining this process as a set of guidelines and / or activities to support a target group of people in performing their tasks. More specific, but aligned with the previous definitions, Mirakyan and De Guio [4] define urban energy planning as the process of finding solutions to the best mix of energy demand and supply in a given area. The solution shall support a sustainable development of the area in a long-term run, and at the same time shall be socially acceptable and institutionally sound. Regarding the nature of the process, this definition [4] emphasizes on the fact that urban energy planning is a participatory and transparent process. It offers the opportunity to the planners to simplify and present complex issues in a structured way, taking account the system as a whole. Therefore, decision makers have a better understanding of the issues and are supported regarding their planning decisions. The process is structured into four phases (i) Preparation & orientation, (ii) Detailed analysis, (iii) Priorization & Decision, and finally (iv) Implementation & Monitoring.

The findings of this paper are based on the generic urban energy planning process described above, as well as its more detailed description in the form of the Sustainable Energy Action Plan (SEAP) [5,6]. The choice of the SEAP process has been motivated due to its alignment with the generic process [4] described above and its wide use in Europe, more than 6000 users (cities or municipalities) by the end of 2014.

1.2. Urban energy planning support tools

There exist a wide range of energy planning tools that can be used or adapted for decision support in energy planning. The following section lists examples of such tools, including their main characteristics

SUNtool [7] and its later successor CitySim [8] attempt to model and simulate energy flows of buildings. EnerGis [9] calculates the minimum annual heat demands of buildings and displays the results in a georeferenced context. SynCity [10] is a scenario development, simulation, and optimization tool that is used at a city scale. It focuses on urban energy systems and its goal is to identify where it is possible to achieve large reductions concerning the energy intensity of cities. UrbanSim [11,12] is a scenario development and simulation open source tool that is used at a city scale. It has a GIS based results browser and it addresses a city from a holistic perspective. CommunityViz [13], a scenario development and GIS based decision support tool for land-use planning, is an extensions of the GIS software ArcGIS. SEMERGY [14,15] is a decision support tool that is specialized in building refurbishment decision making. It supports decision makers to define strategies concerning the optimization of the configuration of building components by finding an optimal trade-off between energy efficiency and cost.

More comprehensive reviews of energy planning related tools are found in [16] and [17]. However, the focus in this subsection addressed tools that support (or can be adapted to support) the type of urban energy planning processes described above.

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Fig. 1. Design methodology overview.

The tools addressed above provide a certain set of functions that respond to the specific requirements of their different users. Some of them, such as SEMERGY, can also be used in combination with the proposed solution in this paper. However, these tools do not fulfill all at once the main characteristics of urban energy planning support systems, which have been defined in a previous related work [18]: (i) Supporting the perspectives of different involved stakeholders. (ii) Quantifiable impact of developed strategies and simplified presentation of impact (so that it is understood by all the stakeholders). (iii) Integration of the measures that compose the strategy, also in terms of stakeholders’ implication. (iv) Re-usability of the system in different cities that have different data availabilities or stakeholders.

2. Design methodology

The adopted methodology to design the target stakeholder-driven urban energy planning support system is shown in Fig. 1. It is an iterative process that starts with the scoping phase and ends with the data use phase. Each iteration allows the integration of a new measure (e.g. integrating solar photovoltaics in buildings or refurbishing buildings). Stakeholders that are affected by the measures are identified. Questions they raise, which answers influence their potential acceptance or rejection of the measure, are listed. Then, data availability in the city is checked to obtain an overview about the expected level-of-detail of the computation models to be developed or possibly re-used. In the data modeling phase, steps (algorithms) to calculate answers for the questions of the stakeholders are defined. Based on the calculation steps, an extraction of their main key words is performed. These keywords (semantics) represent the parts of the city that are necessary to model in order to answer the questions of the stakeholders, regarding the measures that are within the scope of the system. These semantics are classified within an ontology, following a methodology described in more details in [19]. As the ontology does not perform any computation by itself and only behaves as an integration platform, computation models are still required to calculate answers and populate the ontology with actual data. Computation models are developed based on the calculation steps, defined in the data modeling phase. In the interaction modeling phase, the ontology is extended to integrate the dynamics that exist among itself i.e. what data properties are impacted by which others (e.g. if the efficiency of a solar PV system changes, then the electricity generation of buildings using this system increases as well). In the decision modeling phase, the output of the system is simplified so that all stakeholders understand it. Ranges of values are assigned natural language interpretations (very good, good, bad) and integrated within the ontology. This phase is further explained in the next section. In the data integration phase, the output of the computation models is integrated using the logics and the semantics described in the ontology. Tools such as Karma data integration tool [20] can be used in this phase. Finally, once the data are integrated using the ontology, they are deployed on the web in a Resource

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3. Stakeholder-oriented energy planning support

The above methodology has been applied to develop a stakeholder-oriented urban energy planning support system. The system supports two measures: building-integrated solar PV and building refurbishment. It has been applied to a district in the city of Vienna that includes about 1200 buildings. The questions of the stakeholders that have been addressed, and that the designed system supports, are shown in Table 1. Concerning solar PV, the questions are answered for each single building. However, for building refurbishment, it was possible to answer the questions only at a census district level (group of buildings), due to the lack of data to support calculations at a building-level. An interface overview of the system is shown in Fig.2, where locations are classified according to the implementation of either solar PV or building refurbishment, from several perspectives. The selection of locations for the potential implementation of measure results in a calculation of four indicators: costs, CO2 emissions, energy demand, and renewable energy use.

Table 1. Questions answered by the system.

Stakeholders Questions Solar PV Building

refurbishment Building owner -What is the net present value of my investment?

-What is my investment Break-even duration? -How much investment costs are required?

x x x x x x City administration

-How much subsidies are to be provided? -How much energy is saved?

-How much CO2emissions are saved?

-How much electricity is produced?

x x x x x x

Grid operator -What transformers will be overloaded?

-What is the peak feed-in power at the transformers? -How long does the overload occur?

-What is the electricity feed in quantity?

-How much is the direct use of the generated electricity?

x x x x x

3.1. Supporting single stakeholders regarding single measures

The system classifies buildings or census districts in terms of their suitability for each stakeholder for the potential implementation of a given measure (solar PV or building refurbishment). This classification of locations from each stakeholder’s view-point does not consider any mutual agreement with the other stakeholders. Furthermore, it does not consider the each stakeholder has more than one option (measure) to implement. This is achieved by defining the ranges of values to each question of the stakeholders e.g. any solar PV installation in a building with a net present value more than 25000€ is classified as “very good”.

3.2. Supporting groups of stakeholders regarding single measures

The system classifies buildings or census districts in terms of their suitability for all the stakeholders (mutual agreement) for the potential implementation of a given measure. This classification shows the locations that are

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considered as being “very good” or “good” for all the stakeholders together to implement a given measure. This classification is based on the single stakeholder/single measure classification e.g. locations that are classified as “very good” from the building owners perspectives and “good” from all the others are classified as “very good”. 3.3. Supporting single stakeholders regarding a group of measures

The system classifies census districts in terms of their suitability for each stakeholder for the potential implementation of a certain measure, given that there is an alternative one. This means that the system suggests that the potential implementation of a certain measure is appropriate only if it is better than the other measure. This has been possible to implement only at a census district-level, as no data was available to perform calculation for building refurbishment at a single building-level. This classification has been achieved by finding common questions of the same stakeholder regarding the two measures, combined with the single perspective/single measure classification. For example, a census district is classified as “very good” for solar PV (as a better option) from the building owners perspective if it is (i) already “very good” in the single perspective/single measure classification and (ii) it has a net present value per square meter higher than the one for building refurbishment.

3.4. Supporting groups of stakeholders regarding a group of measures

The system classifies census districts in terms of their suitability from the perspective of all the stakeholders (mutual agreement) for the potential implementation of a certain measure, given that there is an alternative one. This implies that when a census district belongs to this class, it is supposed to be agreed upon (from all involved stakeholders) as “very good” or “good” for the implementation of a certain measure, given that that there is another alternative measure. This is achieved based on the single stakeholder/ group of measures classification e.g. a census district is considered as “very good” (for all stakeholders) for the potential implementation of solar PV if that district is already classified in single stakeholder/ group of measures classification as “very good” for both the building owners and the city administration.

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their perceptions regarding the usefulness of the measures- in terms of costs and effects. It provides different stakeholders with specific answers to their view-points. Furthermore, it anticipates their decisions and presents them in a user-friendly visual way through simple icons: locations are classified as “very good”, “good”, or “bad” for a potential implementation of solar PV or building refurbishment. This classification considers that the stakeholders have different interests and classifies as well locations in terms of their satisfactions to all stakeholders interests (besides the single-stakeholder classification). The classification of locations considers the mutual “competition” of the two measures i.e. one could be better than the other either for a single stakeholder or all of them together. The selection of locations for the implementation of a certain measure results in an impact assessment on the city, in terms of costs (broken-down to a stakeholder-level), CO2 emissions, energy demand, and renewable energy use. The system includes update mechanisms, in the case better data sets become available, or if to be used in other cities with different data availability and stakeholders. Future work regarding this system includes its extension to include more measures, so that it provides a wider range of options to support the development of urban energy strategies.

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