280942 A Semantic Representation of Policy Goals in the Modeling of Electricity Generation and Water Treatment Systems

Monday, October 29, 2012: 3:45 PM
323 (Convention Center )
Aidid Chee Tahir and Rene Banares-Alcantara, Department of Engineering Science, University of Oxford, Oxford, United Kingdom

Optimization models are used to evaluate different scenarios during the formulation of energy and water policies, including those formulated to ensure electricity and water supply security and sustainability.  The strength of these models lies in their theoretical foundations built on mathematical equations that process numerical (quantitative) data.  Nevertheless, a complete consideration of energy and water policy issues also requires the evaluation of non-numerical (qualitative) data.  For example, MARKAL, MESSAGE and DNE-21 are optimization tools that aid in the evaluation of energy policies, but their use requires the intervention of modelers to "translate" the policy goals set by the policy maker into sets of equations and constraints that are solvable by mathematical procedures.

This paper introduces a modeling environment that can be used for policy analysis and evaluation. This new modeling system aims to support policy makers by automatically converting their policy goals and targets as parts of an optimization model consisting of a set of mathematical expressions. To facilitate the inclusion of these additional aspects in a computer model, a modeling system, which operates at a level that is above that of optimization models, is proposed.

The modeling system integrates semantic representation techniques and knowledge inference functionalities with an optimization model and its solver.  The selected method for semantic representation is a formalism known as ontologies which encodes knowledge in a manner that can be communicated and shared between people and software tools.  Ontologies add semantics through a collection of associated terms, their conceptual relations and a set of logical axioms.  In addition, in this work we have integrated this semantic representation to a suite of engineering models (mathematical equations).

Case study: Prototype Electricity Generation Modeling System

The use of ontologies within the area of energy modeling is a new development.  During the modeling task a modeler is required to translate the intention of the policy maker, as defined by a set of policy goals and targets, into mathematical equivalents fit for input into the electricity generation model.

An added complication is that a comprehensive formulation of an electricity generation mix must include aspects associated with the triple bottom line sustainability (social, environmental and economic criteria), an evaluation of which requires the consideration of a significant amount of diverse non-numerical information.

Two prerequisites must be fulfilled for energy models to consider the whole spectrum of sustainability aspects.  First, the information associated with sustainability in the context of energy policies must be identified and defined.  Second, a new approach to optimization-based energy modeling, which considers both quantitative data and qualitative information, must be developed.

We have developed a prototype system that uses a semantic representation of energy policy knowledge containing not only quantitative data related to technical, economics and environmental aspects, but also qualitative information related to social and political issues.  Goals associated with sustainability issues are translated, via the ontology, to equality and inequality constraints using description logic.  Currently, goals can be set with regards to the following criteria: capital cost, life-cycle energy payback, CO2 emissions reduction, land utilization, water conservation, public health risk, new jobs creation, social acceptability and energy supply security.  The semantic representation is integrated into a prototype energy modeling system used to formulate an optimized electricity generation mix.

A scenario-based analysis has been chosen to incorporate the uncertainties associated with the future.  In addition, a bottom up approach was chosen to represent technology from an engineering point of view.  Furthermore, optimization was selected as the most appropriate method for the allocation of energy resources.

The Prototype Energy Modeling System uses a semantic representation module which consists of four ontologies with links to engineering models (e.g. mass and energy balances).  Representation of the core knowledge is formed by the Energy Policy Ontology and the Value Partition Ontology with support from engineering models, while the Scenarios Ontology and Equation Construction Ontology constitute the representation of the application knowledge.

To demonstrate the Prototype Energy Modeling System, an example case study for the formulation of an electricity generation mix in Malaysia was conducted, which explores the influence of our extended set of policy goals and targets on the optimization of electricity generation mixes, i.e. the selection of type of electricity generation technologies and their quantity over a time horizon.  The case study illustrates how through the use of logic inference, the Prototype Energy Modeling System can formulate mathematical expressions to be used as an input to the optimization of an electricity generation mix (the objective function, day and night electricity generation requirements, renewable energy generation limits due to intermittency, and heat and power requirements).  It demonstrates that for a set of scenario drivers (population size and growth, industrialization levels, GDP growth and decommissioning rates), an electricity generation mix that fulfils the constraints can be obtained.  There was a fair degree of similarity between the results generated from the Energy Model created by the Prototype Energy Modeling System and the official Malaysian government plans, which lends credence to the capability of the Prototype Energy Modeling System to formulate electricity generation mixes that are acceptable and believable.

Case study: Prototype Water Modeling System

Similarly, optimization models aid in the formulation of water policies, including those designed to ensure water supply security and sustainability.

A prototype water modeling system based on a semantic representation of water policy has also been developed.  The objective of this second case study was to demonstrate the generality of the overall architecture of the system: the Energy Ontology was substituted by a Water Ontology, the library of electricity generation engineering models was substituted by its equivalent for water treatment and supply, and the user interface was modified to reflect the new application (the new set of goals).  The rest of the system, i.e. the Knowledge Inference, Equation Builder and Linear Programming modules were not modified.  As a result it took approximately only four weeks to develop the Prototype Water Modeling System.

To demonstrate the concept of the second prototype, a case study for the state of Penang in Malaysia was developed.  From a set of water policy goals, a water model was created automatically and subsequently optimized to generate a water treatment and supply system.

Discussion

         The Optimization Modeling System presented in this paper, addresses the disconnect between the policy maker and the optimization model itself.  We have sought to extend the boundaries of the formulation of optimization models used for policy evaluation and analysis with particular emphasis given to energy and water models.  This has been achieved by semi-automating part of the function and the associated tasks of the modeler.  This modeling system creates a Model by automatically formulating the necessary mathematical expressions for optimization based on both quantitative and qualitative inputs from the user.  The modeling system achieves this task through the combined use of a semantic representation, knowledge inference, mathematical techniques and procedural programming.

         Our modeling systems breaks new ground with regards to the use of ontologies.  As far as can be ascertained, this is the first use of ontologies as a method to formulate mathematical expressions through inference in the knowledge domain.  This allows for a reduced dependence on manual programming of mathematical expressions where human intervention results in lack of uniformity and human error can cause inaccurate results.  From an application standpoint, the Prototype Modeling Systems and the Models formulated offer the advantage of a single evaluation platform, explicit model documentation, rapid model reconfiguration, ease of use, and an opportunity to explore new results.

         Our modeling systems however do have limitations. In particular, while the Optimization Modeling System accepts qualitative inputs, the analysis of these inputs using qualitative data remains limited.  From an application standpoint, the ability of the Optimization Modeling System to process qualitative data is limited by the richness of the ontology, which in turn is limited by the available knowledge for codification.  In addition, the Prototype Modeling System and the Model occasionally suffer from the vulnerability of an infeasible solution space for which only limited problem finding and solving functions have been incorporated.

         Our proposed modeling system would benefit from additional research in the areas of semantic representation and its integration to linear programming.  The four areas of improvement are: (a) the expansion of the knowledge codification paths, (b) the improvement in the integration between the ontology and the engineering models, (c) the enhancement of the representation quality of the ontology, and (d) the adoption of a better multiobjective function optimization method.

         In conclusion, we view the prototype modeling systems introduced in this paper as a proof-of-concept showing their potential use by policy makers in their efforts to formulate, analyze and evaluate sustainable and effective policies.


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