Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

Carbon-constrained energy planning for integrated transportation and power generation sectors

The introduction of electric vehicles (EV) has changed the transportation and power generation systems, mainly affecting energy production, energy efficiency, and overall grid performance. In Malaysia, the government stated its commitment to adopt green initiatives and sustainable development. Thus, this research presents the energy planning framework for power generation and transportation system which determines the optimal energy mix by utilizing available renewable energy resources and the best location for charging stations. This research utilized carbon emission pinch analysis (CEPA) as a baseline model to conduct a feasibility study for electric vehicles in Malaysia. Mathematical equations were then applied to develop a mixed-integer linear programming model incorporated complex constraints for further holistic analysis of Malaysia. Four scenarios were devised to explore the impact of different carbon emission mitigation strategies. The results show that Scenario 4 (S4), which considered 40 % of total carbon emission reduction come from transportation sector, provide the best option in terms of energy mix, technology selection, levelized cost of electricity, and operation of EV. Although it requires the highest number of EV on the road compared to other scenario which is 2,345,776 units, it will only utilize 66,260.61 GWh of energy to be generated from renewable energy which is the lowest compared to the other scenarios. This results in the lowest levelized cost of electricity which is 0.3364 RM/kWh. This tariff can be applied to lower the cost of charging for EV operation. This research also provides strategies for the government to implement electric vehicles in Malaysia. The models may also be converted into useful software for town planners and policymakers

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Grid-able Plug-in Electric Vehicles in Smart Grids: Incorporation into Demand Response

Electric transportation has attracted a great deal of interest within the transport sector because of its notable potential to become a low-carbon substitute for conventional combustion engine vehicles. However, widespread use of this form of transportation, such as plug-in electric vehicles (PEVs), will constitute a significant draw on power grids, especially when associated with uncontrolled charging schemes. In fact, electric utilities are unable to control individual PEVs in order to manage their charging and avoid negative consequences for distribution lines. However, a control strategy could be directed at a single vehicle or group of vehicles. One effective approach could be to build on a supervisory control system, similar to a SCADA system that manages the aggregation of PEVs, a role that could be filled by aggregators that exchange data and information among individual PEVs and energy service providers. An additional consideration is that advances in intelligent technologies and expert systems have introduced a range of flexible control strategies, which make smart grid implementation more attractive and viable for the power industry. These developments have been accompanied by the initiation of a new paradigm for controllable PEV loads based on a number of advantages associated with a smart grid context. One of the established goals related to smart grids is to build on their ability to take advantage of all available energy resources through efficient, decentralized management. To this end, utilities worldwide are using IT, communication, and sensors to provide enhanced incorporation of operational tools and thus create a more robust and interactive environment able to handle generation-demand dynamics and uncertainties. One of these tools is demand response (DR), a feature that adjusts customers’ electricity usage through the offer of incentive payments. Motivated by this background, the goal of the work presented in this thesis was to introduce new operational algorithms that facilitate the charging of PEVs and the employment of their batteries for short-term grid support of active power. To allow both public parking lots and small residential garages to benefit from smart charging for end-user DR, a framework has been developed in which the aggregator handles decision-making through real-time interactions with PEV owners. Two interaction levels are implemented. First, for charging coordination with only one-round interaction, a fuzzy expert system prioritizes PEVs to determine the order in which they will be charged. Next, for smart charging, which includes battery discharging, a multi-stage decision-making approach with two-round interaction is proposed. Real-time interaction provides owners with an appropriate scheme for contributing to DR, while avoiding the inconvenience of pre-signed long-term contracts. A new stochastic model predicts future PEV arrivals and their energy demand through a combination of an artificial neural network (ANN) and a Markov chain. A new method is proposed for promoting collaboration of PEVs and photovoltaic (PV) panels. This technique is based on a determination of the ways in which smart charging can support simultaneous efficient energy delivery and phase-unbalance mitigation in a three-phase LV system. Simulation results derived from 38-bus and 123-bus distribution test systems have verified the efficacy of the proposed methods. Through case-study comparisons, the inefficiency of conventional charging regimes has been confirmed and the effectiveness of real-time interactions with vehicle owners through DR has been demonstrated. The most obvious finding to emerge from this study is that the use of a scoring-based (SCR) solution facilitates the ability of an aggregator to address urgent PEV energy demands, especially in large parking lots characterized by high levels of hourly vehicle transactions. The results of this study also indicate that significantly greater energy efficiency could be achieved through the discharging of PEV batteries when PEV grid penetration is high

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue