Exploration of dispatch model integrating wind generators and electric vehicles

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

Stochastic two-stage reliability-based Security Constrained Unit Commitment in smart grid environment

This dissertation contributes to our understanding of how plug-in hybrid electric vehicles (PHEVs) and plug-in battery-only electric vehicles (EVs)—collectively termed plug-in electric vehicles (PEVs)—could be successfully integrated with the electric power system. The research addresses issues at a diverse range of levels pertaining to light-duty vehicles, which account for the majority of highway vehicle miles traveled, energy consumed by highway travel modes, and carbon dioxide emissions from on-road sources. Specifically, the following topics are investigated: (i) On-board power electronics topologies for bidirectional vehicle-to-grid and grid-to-vehicle power transfer; (ii) The estimation of the electric energy and power consumption by fleets of light-duty PEVs; (iii) An operating framework for the scheduling and dispatch of electric power by PEV aggregators; (iv) The pricing of electricity by PHEV aggregators and how it affects the decision-making process of a cost-conscious PHEV owner; (v) The impacts on distribution systems from PEVs under aggregator control; (vi) The modeling of light-duty PEVs for long-term energy and transportation planning at a national scale.

Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. To this end, we propose a fully distributed algorithm to solve the PEVs' Cooperative Charging with Power constraints (PEV-CCP). Our solution considers the electric power limits that originate from physical characteristics of charging station, such as on-site transformer capacity limit, and allows for containing charging burden of PEVs on the electric distribution network. Our approach also enables plug-and-play and valley-filling capabilities of PEV charge scheduling. It distributes computation among agents (PEVs) to solve the PEV-CCP problem in a distributed fashion through an iterative interaction between neighboring agents. The structure of each agent's update functions ensures an agreement on a price signal while enforcing individual PEV constraints. In addition to converging towards the globally-optimum solution, our algorithm ensures the feasibility of each PEV's decision at each iteration. We have tested performance of the proposed approach using a fleet of PEVs. Simulation results verify the plug-and-play capability of the proposed approach by efficiently tracking the changes in terms of arrival/depart of PEVs.

This paper looks into the consumption side of the power balance, and more specifically on the effects of utilizing an increasingly larger fleet of Plug-in electric vehicles (PEVs) for personal transportation. To asses this, an Agent Based Model of PEVs has been extended and developed with different charging strategies. The model simulates power demand from a given number of PEVs in a given area, and may be useful for policymakers and researchers alike. Simulations ran for the city of Trondheim reinforce the notion that the rising adoption of PEVs might not only pose a substantial challenge due to the relative size of the power demanded, but more critically also because of the variability that the charging profiles exhibit. On the other hand, the different behaviour of the PEV agents, as modelled through different charging strategies, indicate that incentives such as price signals might effect how much the agent charge at different times. Hence it may even lend the PEVs batteries as assets to help stabilize the power balance in the electric grid.

Bidirectional coordinating dispatch of large-scale V2G in a future smart grid using complementarity optimization

Plug-in electric vehicle (PEVs) in demand response programs (DRPs) have indicated a great potential to enhance power system reliability by reducing their charging demand. However, PEVs often fail to reduce their load due to some external factor. Herein, an analytical approach for reliability modeling of aggregated PEVs, the so-called electric vehicle fleet (EVF), is presented. A systematic method based upon frequency and duration approach is utilized to model an EVF similar to that of a multi-state conventional unit, where the probability, frequency of occurrence, and departure rate of each state can be acquired. The proposed methodology is capable of considering uncertainty of customer participation in DRPs for both flexible and inflexible PEVs. Interval data, which is the core deliverable of advanced metering infrastructure (AMI), is essential for PEVs to participate in DRPs. Thus, the impact of AMI failure is investigated on the implementation of EVFs in reliability assessment. Several important factors such as different penetration level of EVFs are taken into account in the analysis. The hourly historical data is employed to achieve the customers participation regime. The Roy Billiton test system and IEEE 24-bus system are used to trace the capability of proposed EVF reliability model.

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique.

To ensure sufficient investment in electricity generation capacity, mechanisms such as strategic reserves are being considered or already implemented. We analyze the effectiveness of a strategic reserve in the presence of a growing portfolio share of renewable energy sources (RES) with EMLab-Generation, an agent-based electricity market model. A strategic reserve can stabilize investment, but within limits. Uncertainty regarding future demand may cause the market to become instable, potentially leading to periods with very high electricity prices. In the presence of a large share of variable renewable energy sources, the reserve design should be adjusted or replaced by an alternative capacity mechanism.

The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications.

The global energy crisis and the pursuit of carbon neutrality have introduced significant challenges to the optimal dispatch of power systems. Despite advancements in optimization techniques, existing methods often struggle to efficiently handle the uncertainties introduced by renewable energy sources and the dynamic behavior of plug-in electric vehicles (PEVs). This study presents a multi-energy collaborative optimization approach based on a dynamic opposite level-based learning optimization swarm algorithm (DO3LSO). The methodology explores the impact of integrating PEVs and renewable energy sources, including photovoltaic and wind power, on unit commitment (UC) problems. By incorporating the bidirectional charging and discharging capabilities of PEVs and addressing the volatility of renewable energy, the proposed method demonstrates the ability to reduce reliance on traditional fossil fuel power generation, decrease carbon emissions, stabilize power output, and achieve a 7.01% reduction in costs. Comparative analysis with other optimization algorithms highlights the effectiveness of DO3LSO in achieving rapid convergence and precise optimization through hierarchical learning and dynamic opposite strategies, showcasing superior adaptability in complex load scenarios. The findings underscore the importance of multi-energy collaborative optimization as a pivotal solution for addressing the energy crisis, facilitating low-carbon transitions, and providing essential support for the development of intelligent and sustainable power systems.

This paper investigates the impact of Plug-in Electric Vehicles (PEVs) on the voltage improvement and loss reduction in MicroGrids (MG). The proposed model in this paper aims to minimize the total expected voltage deviation of the MG, while being subject to economic policies. The studied MG consists of PEVs, Renewable Energy Sources (RES), Energy Storage Systems (ESSs), and Distributed Generations (DGs), and the MG is able to participate in day-ahead energy markets. The uncertainties in arrival and departure time of PEVs, RES outputs, and day-ahead market prices are stochastically taken into account and the PEVs are modelled to be capable of charging or discharging active power and/or absorbing or injecting reactive power. A four-quadrant operation mode of a PEV is developed in this paper and incorporated in a mix-integer non-linear programming (MINLP) optimization formulation. The advantages of exploiting PEVs in an MG for enhancing voltage profile and loss reductions are then scrutinized. In order to demonstrate the effectiveness of the proposed model, various case studies are conducted on a modified 18-bust IEEE test system.

Plug-in electric vehicles (PEVs) could provide valuable ancillary service for power systems with intermittent renewables. This paper proposes a charging model of a large amount of PEVs to mitigate the high-level wind power fluctuations in a distribution network so as to regulate system frequency. With the prerequisite that all PEV daily driving patterns are completely satisfied, the PEV charging power is economically allocated to counterbalance the wind generation intermittency. Afterward, a center-free control scheme based on the consensus algorithm is designed for PEVs to share the fluctuating wind generation in a fully distributed manner. The scheme is robust and flexible to practical PEV charging behaviors including arrival and departure time, initial and desired SOC, as well as the frequently changed departure time. Comprehensive simulations on a distribution system with coal and diesel generators, several wind farms and 2000 PEVs demonstrate that the proposed PEV charging scheme could effectively regulate the system frequency in the real time while the PEV users’ charging requirements could be flexibly satisfied.

An empirically-validated methodology to simulate electricity demand for electric vehicle charging