Q.G. Lin

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.

IPEM: An Interval-parameter Energy Systems Planning Model

Abstract Energy systems planning models are specifically developed for effective planning of energy activities in a regional, national, or global context. However, the planning process is fraught with uncertainties that may affect the effectiveness of the planning. In this study, an interval-parameter linear programming approach is introduced to develop an interval-parameter energy systems model (IPEM) for supporting effective regional energy systems planning under uncertainty. The developed methodology is then applied to a hypothetical regional energy system. The results strongly suggest that this innovative approach can effectively handle the uncertain information expressed as intervals in the energy planning process and provide more satisfactory solutions for the optimization problem of energy allocation and capacity expansion within a regional jurisdiction. Compared with other energy systems models, this model generates two schemes corresponding to the upper and the lower bounds of system objective, which represent two extreme decisions regarding environmental-economic trade-off. The interval solutions allow for detailed interpretation of the trade-off between environmental pollution risks and economic objectives.

High-Resolution Probabilistic Projections of Temperature Changes over Ontario, Canada

Planning of mitigation and adaptation strategies to a changing climate can benefit from a good understanding of climate change impacts on human life and local society, which leads to an increasing requirement for reliable projections of future climate change at regional scales. This paper presents an ensemble of high-resolution regional climate simulations for the province of Ontario, Canada, developed with the Providing Regional Climates for Impacts Studies (PRECIS) modeling system. A Bayesian statistical model is proposed through an advance to the method proposed by Tebaldi et al. for generating probabilistic projections of temperature changes at gridpoint scale by treating the unknown quantities of interest as random variables to quantify their uncertainties in a statistical way. Observations for present climate and simulations from the ensemble are fed into the statistical model to derive posterior distributions of all the uncertain quantities through a Markov chain Monte Carlo (MCMC) sampling algorithm. Detailed analyses at 12 selected weather stations are conducted to investigate the practical significance of the proposed statistical model. Following that, maps of projected temperature changes at different probability levels are presented to help understand the spatial patterns across the entire province. The analysis shows that there is likely to be a significant warming trend throughout the twenty-first century. It also suggests that people in Ontario are very likely to suffer a change greater than 28C to mean temperature in the forthcoming decades and very unlikely to suffer a change greater than 108C to the end of this century.

EMDSS: An optimization-based decision support system for energy systems management under changing climate conditions - An application to the Toronto-Niagara Region, Canada

Management of energy systems is a challenging task, involving a large number of social, economic, environmental, technical, and political factors. This challenge is being complicated with growing concerns of climate change impacts on various natural processes and human activities. To comprehensively deal with such complexities, system analysis approach are desired as it can address various impact factors and facilitate the assessment of policy consequence and climate change response within an interactive energy management systems. The objective of this study is to develop an optimization-based decision support system (EMDSS) and the relevant software package to provide comprehensive analysis of climate change impacts, and energy and environmental policy responses within an energy management system framework. EMDSS is then applied to the Toronto-Niagara Region (TNR), Canada. The results indicated that EMDSS was effective for analyzing and visualizing various scenarios within energy management systems. The obtained information can direct policy makers to initiate strategies for dealing with various issues of climatic change, energy development and environmental management.

CCEM: A City-cluster Energy Systems Planning Model

Abstract An understanding of complex interactions among energy, environmental, and economic activities is important for making decisions to support sustainable economic development and environmental protection. Energy models at global, national, and provincial levels are effective tools used for examining these interactions. However, these models are inadequate for a city-cluster jurisdiction such as the Toronto-Niagara Region (TNR), Canada, which has unique economic and energy characteristics. The objective of this study is to develop a City-cluster Energy Systems Planning Model (CCEM) and apply it to the TNR as a case study. It is demonstrated that the model can be effectively used for supporting energy planning, environmental management, and greenhouse gas (GHG) reduction in a city-cluster jurisdiction.

The Optimization of Energy Systems under Changing Policies of Greenhouse-gas Emission Control—A Study for the Province of Saskatchewan, Canada

Abstract Since most of the increased greenhouse gas emissions can be attributed to activities associated with production and consumption of energy, reducing greenhouse gas emission will inevitably affect the energy industry significantly. In this study, policy cases for reflecting greenhouse gas-emission reduction and energy development in the Province of Saskatchewan, Canada are investigated. The impacts of the Kyoto target on Saskatchewans energy system are analyzed; the least-cost strategies for dealing with greenhouse gas-emission reduction in a long-term horizon are developed. The modeling results suggested that the developed model was capable of supporting in-depth analyses for energy-related activities in response to the greenhouse gas-emission reduction target and could provide effective support for the formulation of the Provinces energy polices (utilization of renewable and nuclear power) and strategies in dealing with climate change issues.

An Interval-Parameter Chance-Constraint Mixed-Integer Programming for Energy Systems Planning Under Uncertainty

Abstract Energy management systems are fraught with uncertainties. Such uncertainties may be expressed by interval numbers or probability distributions. In addition, issues of capacity expansion related to timing, sizing, and siting under such uncertainties need to be addressed. In this article, an interval-parameter chance-constraint mixed-integer programming (ICCP) is developed to tackle highly uncertain problems in energy management systems through integrating interval-parameter linear programming and chance-constraint programming. The developed model is then applied to a regional energy system. The results indicate that ICCP can effectively deal with uncertain information in energy management systems.

Inexact Community-Scale Energy Systems Planning Model

Energy systems planning models are useful for supporting decisions of urban energy systems planning and environmental management. The previous studies on energy systems modeling were too aggregated to reach insight into the interactive characteristics of energy-related activities at a community level, and thus were unable to address the unique environmental and economic features associated with community-scale energy management systems. In addition, they could hardly deal with multiple uncertainties expressed as interval values and probabilistic distributions. Therefore, the objective of this study is to develop an interval-parameter chance-constraint community-scale energy systems planning model (IPC-CEM) for supporting energy and environmental systems management under uncertainty. IPC-CEM will then be applied to the planning of a community-scale energy system to demonstrate its applicability. The results indicated that the developed model had advantages in reflecting complexities of various uncertainties as well as dealing with problems of urban infrastructure development and greenhouse gas-emission management within community-scale energy management systems.

Investigating future precipitation changes over China through a high‐resolution regional climate model ensemble

Due to climate change, rising temperature around the world will have a great potential to influence the global hydrologic cycle, thus leading to substantial changes in the spatial and temporal patterns of precipitation. In this study, the effects of global warming on the regional hydrologic cycle, particularly on the spatiotemporal patterns of precipitation, over China are investigated through a high-resolution regional climate ensemble. In detail, the PRECIS regional climate modeling system is employed to simulate the regional climate over China from 1950 to 2099 with a fine resolution of 25u2009km, driven by the boundary conditions from a four-member HadCM3-based perturbed-physics ensemble (i.e., HadCM3Q0, Q1, Q7, and Q13) and the ECHAM5 model. Historical simulations of the PRECIS ensemble are first compared to the observations to validate its performance in capturing both the spatial and temporal patterns of precipitation. The comparisons show that the PRECIS ensemble is likely to overestimate precipitation in the south and exhibits slight dry biases in the northwest and southeast coasts of China. The projections from the PRECIS ensemble for future periods (i.e., 2020s, 2050s, and 2080s) are then analyzed to help understand how the regional characteristics of precipitation will be affected in the context of global warming. It is shown that the annual mean precipitation over China is likely to increase throughout the 21st century (i.e., by 0.078u2009mm/d in 2020s, 0.218u2009mm/d in 2050s, and 0.360u2009mm/d in 2080s). This may suggest that the rising temperature due to climate change will intensify the regional hydrologic cycles in China. However, apparent spatial and temporal variations are also reported in the projected precipitations from the PRECIS ensemble. For example, bigger changes in precipitation are usually observed in summer; projected precipitation changes in the southeast are apparently higher than other regions. In addition, the results show that the fluctuation range of the ensemble simulations will increase with time periods from 2020s to 2080s, indicating that the longer the projecting periods, the more uncertain the projections will be.

Dynamic Planning of Water Resource and Electric Power Systems under Uncertainty

AbstractHydropower plays an important role in electric power systems. It not only interacts with many non-hydropower generation activities but also competes with other water users (industrial, commercial, residential, and agricultural) for limited water resources. Therefore, the objective of this study is to investigate an optimized water allocation scheme within a water resource and electric power management system through developing an inexact water resource and electric power systems planning model (WPEM). WPEM is based on the interval-parameter programming and mixed-integer programming techniques; thus, it can deal with dynamics of capacity expansion and uncertainties associated with system management. The developed method is then applied to a power system with water shortage issues in the future. The results of scenario analysis indicate that WPEM could help get insights into the tradeoff between system benefit and water utilization as well as that between system benefit and environmental concern. Th...