EV Scheduling Framework for Peak Demand Management in LV Residential Networks

Increased electrification in the residential and transport sectors is changing the energy demand profiles significantly, which results in reshaped peak demand. These changes in demand profiles can cause grid overloading and jeopardize network reliability especially when the excessive use of electricity within a network is uncoordinated. In this article, an aggregated coordination mechanism is proposed for electric vehicle (EV) charge–discharge scheduling to manage the peak demand in the low-voltage (LV) residential networks. The proposed model uses mixed-integer-programming-based optimization approach to minimize the cost of energy while managing the peak demand and complying with grid constraints. A stochastic model is presented to account for the uncertainties associated with forecast inaccuracies of the day-ahead scheduling. The proposed strategy is assessed by means of simulation studies considering an LV residential neighborhood in Sydney, Australia. The results indicate the effectiveness of the proposed strategy to minimize the cost of electricity for the EV owners while managing the peak demand for the grid operators. Comparison with the state-of-the-art EV scheduling strategies indicates that the proposed strategy can improve the load factor of the local network up to 36%, the peak-to-average ratio up to 27%, and cost reductions up to 56%

A Fast Time-Domain Current Harmonic Extraction Algorithm for Power Quality Improvement Using Three-Phase Active Power Filter

© 2013 IEEE. Harmonic current estimation is the key aspect of Active Power Filter (APF) control algorithms to generate a reference current for harmonic compensation. This paper proposes a novel structure for harmonic current estimation scheme based on Trigonometric Orthogonal Principle (TOP) and Self Tuning Filter (STF). The key advantages of the proposed method are its simplicity, low computational burden and faster execution time in comparison to the conventional harmonic current estimation approaches. The TOP method provides a simple and fast approach to extract the reference current, while STF provides a simplified structure to generate the required synchronization signal that eliminates the need of a Phase Locked Loop (PLL) algorithm for synchronization. As a result, it exhibits less complexity in implementation and less consumption of microcontroller's resources; thus, the proposed method can be implemented using a low-cost microcontroller. It is shown in the paper that the proposed method provides 10 times gain in processing speed as compared to the conventional DQ method. The proposed approach is analyzed in detail, and its effectiveness and superior performance are verified using simulation and experimental results

Novel sizing method of energy storage system considering intermittent usage of EVs in a constrained grid

© 2020 IEEE. Charging of electric vehicles (EVs) significantly impact the reliability of the power system. A constrained power grid is a feasible solution to maintain the reliability of the power system. However, in a constrained power grid, it is challenging for the parking lot operator to balance the additional load. The fast and high-power density of batteries makes them a conceivable option for this task if adequately sized. A sizing algorithm is proposed to compute the battery capacity for parking lots while considering the intermittent usage of EVs in a constrained grid. Charging profile of EVs is constructed by considering travel pattern, charging need and driver's behaviour of EVs. The proposed sizing algorithm avoided over/under-sizing of the battery energy storage system and fulfilled the EV charging demand in the parking lot. The accuracy of the proposed battery sizing algorithm is shown by simulation results, characterized by real data of household travel survey and parking occupancy data

Anti-islanding method for houses equipped with electric vehicles and photovoltaic system

© 2020 IEEE. Integration of electric vehicles (EVs) are exponentially increasing in the global market and by enabling vehicle-to-grid (V2G) EVs can inject power back into the grid. However, in an event of unintentional islanding, injecting power into the grid may causes potential safety threats to people, equipment, and power system. This paper proposes an adaptive reactive power mismatch method to detect islanding events. When islanding occurs, the proposed method drifts the system frequency away from the nominal value. Then the islanding event is detected based on frequency variations. Results show that the proposed method effectively detects islanding event within 0.801 milliseconds and have negligible non-detection zone

A customer-based-strategy to minimize the cost of energy consumption by optimal utilization of energy resources in an apartment building

© Published under licence by IOP Publishing Ltd. Global energy consumption in heating and cooling of buildings and in the transport sector together accounts for approximately two-thirds of total energy consumption. Consequently, it is important to maximize the use of renewable generation energy in these sectors, and to optimize the use of that energy by managing diverse sources and loads. This is particularly challenging in high-density residential premises where the space for such infrastructure is limited, and storage can have significant impact on energy utilization and demand. In this paper, we describe a customer-based strategy (CBS) to optimize the usage of the available energy resources in such scenarios. The effectiveness of the strategy was validated for an apartment block of 20 households with photovoltaic generation (PV) and stationary battery storage (BS) systems, each with a vehicle-to-grid (V2G) capable electric vehicle (EV). The modelling used real data for customer demand and included the cost of battery degradation and expected vehicle usage in optimizing resource scheduling. Substantial savings in energy costs were shown to be possible for each customer

A Multi-agent system based residential electric vehicle management system for grid-support service

© 2019 IEEE. With a spike in popularity and sales, the electric vehicles (EVs) have revolutionized the transportation industry. As EV technology advances, the EVs are becoming more accessible and affordable. Therefore, a rapid proliferation of light-duty EVs have been noticed in the residential sector. Even though the increased charging demand of EVs is manageable in large-scale, the low-voltage (LV) residential networks might not be capable of managing localized capacity issues of large scale EV integration. Dynamic electricity tariff coupled with demand response and smart charging management can provide grid assistance to some extent. However, uncoordinated charging, if clustered in a residential distribution feeder, can risk grid assets because of overloading and can even jeopardize the reliability of the network by violating voltage constraints. This paper proposes a coordinated residential EV management system for power grid support. Charging and discharging of residential EV batteries are coordinated and optimized to address grid overloading during peak demand periods and voltage constraint violations. The EV management for grid support is formulated as a mixed-integer programming based optimization problem to minimize the inconveniences of EV owner while providing grid assistance. The proposed methodology is evaluated via a case study based on a residential feeder in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed EV management method for mitigating grid overloading and maintaining desired bus voltages

Optimal sizing of hybrid photovoltaic/diesel/battery nanogrid using a parallel multiobjective PSO-based approach: Application to desert camping in Hafr Al-Batin city in Saudi Arabia

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid