Xiaoran Shi

Continuous-discrete simulation-based decision making framework for solid waste management and recycling programs

Solid waste produced as a by-product of our daily activities poses a major threat to societies as populations grow and economic development advances. Consequently, the effective management of solid waste has become a matter of critical importance for communities. However, solid waste management systems are inherently large-scale, diverse, and subject to many uncertainties, and must serve numerous stakeholders with divergent objectives. In this study, we propose a simulation-based decision-making and optimization framework for the analysis and development of effective solid waste management and recycling programs. The proposed solution includes a database and two main modules: an assessment module and a resource allocation optimization module. The assessment module identifies the sources of uncertainties in the system, which are then parameterized and incorporated into the resource allocation optimization module. The resource allocation optimization module involves a novel discrete-continuous model of the system under consideration, in which the continuous nature of decision variables is maintained while inherently discrete processing and transfer operations are accurately captured. The model operates with respect to the waste types and characteristics, costs, environmental impacts, types, location and capacities of processing facilities, and their technological capabilities. Then, an optimization mechanism embedded in the resource allocation optimization module solves the multi-criteria problem of the allocation of limited resources by simultaneously optimizing all relevant decision variables, evaluating performance in real-time via the model. Here, the optimum solution is considered as the combination of parameters that will lead to the highest recycling rate with minimum cost. The proposed framework has been successfully demonstrated for the Miami-Dade County Solid Waste Management System in the State of Florida.

A DDDAMS framework for real-time load dispatching in power networks

The economic environmental load dispatch problem in power networks aims at producing electricity at the lowest financial and environmental costs. In this paper, we propose a novel real-time dynamic data driven adaptive multi-scale simulation framework (RT-DDDAMS) for efficient real-time dispatching of electricity. The framework includes 1) a discovery procedure where the network is split into sub-networks and prospective fidelities are identified, 2) an RT-DDDAMS platform involving algorithms for state estimation, fidelity selection, and multi-objective optimization alongside with a system simulation; and 3) databases for storing sub-network topologies, fidelities, and selective measurements. The best compromise load dispatch obtained from this framework is then sent to the considered power network for deployment. The proposed framework is illustrated and validated via a modified IEEE-30 bus test system. The experiments reveal that the proposed framework significantly reduces the computational resource usages needed for the reliable power dispatch without compromising the quality of the solutions.

A Dynamic Data-driven Approach for Operation Planning of Microgrids☆

Abstract Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the real-time operation plan of an electric microgridwhile considering its conflicting objectives. In particular, the proposed approachis equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scaleidentification procedures; and 3) a multi-objective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scaleof the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstratethe power of the proposed approach in real-time assessment and control of operation in microgrids.

Optimization of distributed generation penetration based on particle filtering

Distributed generation is small scale power cogeneration within an integrated energy network, that provides system wide and environmental benefits. Network benefits include enhancements to reliability, reduction of peak power requirements, improved power quality and enhanced resilience. Environmental benefits include better land use for transmission and distribution, and reduced ecological impact. Deploying distributed generation affects the power loss in the system and has an associated cost. Therefore, optimization of the penetration level of the distributed generation should consider both goals of minimizing total power loss and minimizing total operational costs. In this study, we propose a novel multi-objective optimization framework based on particle filtering to evaluate the effects of adding distributed generation to a networked system in terms of power loss and operational costs, simultaneously. The proposed framework has been demonstrated on the IEEE-30 bus system yielding to minimal power losses of 2.075 MW and minimal costs of

Multi-objective agent-based modeling of single-stream recycling programs

547.51 per hour.