Ahmed Alabdulwahab

Hierarchical Coordination of a Community Microgrid With AC and DC Microgrids

In this paper, a community microgrid with multiple ac and dc microgrids is introduced and analyzed. Individual microgrids with different frequency and voltage requirements would operate as self-controlled entities, which could also cooperate with neighboring microgrids for providing back-up operations in the community microgrid. A hierarchical coordination strategy with primary, secondary, and tertiary coordination is proposed for the economic operation of an islanded community microgrid. The hierarchical strategy is also applied to a grid-connected community microgrid and the results are discussed. The simulation results verify that the proposed hierarchical coordination strategy is an effective and efficient way for coordinating microgrid flows in an islanded community microgrid, while maintaining the rated frequency and voltage with each microgrid. The simulation results also demonstrate the economic operation of a grid-connected community microgrid in which individual microgrids operate as autonomous agents, while satisfying the community objectives.

Optimal Expansion Planning of Energy Hub With Multiple Energy Infrastructures

This paper presents an optimal expansion planning model for an energy hub with multiple energy systems. Energy hub represents a coupling among various energy infrastructures for supplying electricity, natural gas, and heating loads. Combined heat and power (CHP) and natural gas furnaces are considered within the energy hub to convert energy into other forms. The multiple energy system planning problem would optimally determine appropriate investment candidates for generating units, transmission lines, natural gas furnaces, and CHPs that satisfy electricity and heating load forecasts and hub system constraints. The system performances associated with reliability, energy efficiency, and emission matrices is evaluated for the identified planning schedules. Numerical simulations demonstrate the effectiveness of the proposed multiple energy system expansion planning approach based on energy hub.

Coordination of Interdependent Natural Gas and Electricity Infrastructures for Firming the Variability of Wind Energy in Stochastic Day-Ahead Scheduling

In this paper, the coordination of constrained electricity and natural gas infrastructures is considered for firming the variability of wind energy in electric power systems. The stochastic security-constrained unit commitment is applied for minimizing the expected operation cost in the day-ahead scheduling of power grid. The low cost and sustainable wind energy could substitute natural gas-fired units, which are constrained by fuel availability and emission. Also, the flexibility and quick ramping capability of natural gas units could firm the variability of wind energy. The electricity and natural gas network constraints are considered in the proposed model (referred to as EGTran) and Benders decomposition is adopted to check the natural gas network feasibility. The autoregressive moving average (ARMA) time-series model is used to simulate wind speed forecast errors in multiple Monte Carlo scenarios. Illustrative examples demonstrate the effectiveness of EGTran for firming the variable wind energy by coordinating the constrained electricity and natural gas delivery systems.

Optimal Interconnection Planning of Community Microgrids With Renewable Energy Sources

The optimal planning of the interconnected network of multimicrogrids is discussed in this paper. The interconnection planning will enhance the reliability and the economic operation of a community of microgrids. The proposed approach will apply a probabilistic minimal cut-set-based iterative methodology for the optimal planning of interconnection among microgrids with variable renewable energy sources. The optimal planning takes into account various factors including the economics, reliability, and variability of renewables, network- and resource-based uncertainties, and adaptability to accommodate the prevailing operating concerns. A clustering-based method is considered for analyzing the variable data concerning the potential deployment of renewable energy in microgrids. The proposed interconnection planning methodology is applied to a six-microgrid system and the planning results are discussed. The numerical results demonstrate that the proposed interconnection planning methodology will determine an optimal topology accurately and efficiently for a cluster of microgrids, and show that the proposed adaptive planning methodology can easily be applied to practical microgrid applications.

Thermal Generation Flexibility With Ramping Costs and Hourly Demand Response in Stochastic Security-Constrained Scheduling of Variable Energy Sources

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations.

Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.

Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks

This paper studies the role of hourly economic demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly economic demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.

Security-Constrained Co-Optimization Planning of Electricity and Natural Gas Transportation Infrastructures

This paper presents a co-optimization planning model that considers the long-term interdependency of natural gas and electricity infrastructures. The model incorporates the natural gas transportation planning objective in the co-optimization planning of power generation and transmission systems. The co-optimization planning model is decomposed into a least-cost master investment problem for natural gas and electricity systems which interacts with two operation subproblems representing the feasibility (security) and the optimality (economic) of the proposed co-optimization. In addition, the natural gas subproblem would check the feasibility of fuel supply transportation system as part of the proposed co-optimization planning. The co-optimization planning of electricity and natural gas infrastructures would satisfy the desired power system reliability criterion. The iterative process will continue between the co-optimization investment and the operation subproblems until an economic, secure, reliable, and fuel-supply feasible planning for the two interdependent infrastructures is obtained. Numerical simulations demonstrate the effectiveness of the proposed co-optimization planning approach.

Electricity-Natural Gas Operation Planning With Hourly Demand Response for Deployment of Flexible Ramp

This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.

Lexicographic Multiobjective Integer Programming for Optimal and Structurally Minimal Petri Net Supervisors of Automated Manufacturing Systems

Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transitions firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach.