Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-in Electric Vehicle Parking Lots

The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers expected life. In this paper, a new day-ahead co-optimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is minimizing the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results

The integration of distributed energy resources into electric power systems

Small-scale, residential, and distributed energy resources (DER), which are electric vehicles (EVs), heat pumps (HPs), and photovoltaic (PV) arrays, were studied to evaluate their impact on the UK future residential demand and their impact on UK distribution networks. Centralized and decentralized controllers were planned in order to defer reinforcement, while connecting DER units to distribution networks. The centralized controller allocates EV charging durations considering network constraints. The decentralized controller adjusts EV and HP loads based on consumer satisfaction, network constraints, and electricity prices. Normal probability distribution and median filter were used to predict aggregated power of EVs, HPs, and PV arrays on a half-hourly basis over a year. Because of an expected surplus of PV power generation, a considerable demand reduction followed by a sharp demand increase will occur with these residential DER units during summer days in 2035. A low voltage section of test network was used to study the impact of uncontrolled EV charging loads on a three-phase four-wire system. Different combinations of EVs, HPs, and PV arrays were used to investigate their uncertainties in a low voltage section of real network. Real-world trials were used to generate the individual power of residential customers and DER units. Results of unbalanced power flow indicated that network constraints exceeded their limits with a high number of these low carbon technologies. Using an extended section of the test network, the central controller maintains voltage magnitudes, voltage unbalance factors, and power flows within their limits, by re-allocating EV charging durations accordingly. The decentralized controller was designed to minimize electricity bills of EV and HP users. This controller adjusts EV and HP loads to maintain consumer satisfaction and network constraints within their specified boundaries. Consumer satisfaction was determined using mathematical models of EV battery state-of-charge levels and the indoor temperatures of HP houses. The decentralized controller was used to connect predicted numbers of EVs and HPs to a real distribution network, while overcoming the need for network reinforcement, third parties (aggregators), and extensive communication systems

Energy storage and electric vehicles as a means of mitigating uncertainty in urban microgrids

Phd ThesisThe United Kingdom (UK) government intends to end the sale of new conventional petrol and diesel cars by 2040, and Electric Vehicles (EVs) could emerge as the replacement. This is likely to increase the load on electrical distribution networks, while uncontrolled EV charging could increase load forecast uncertainty. Utilising sufficient Energy Storage System (ESS) power to maintain the networks within their power flow and voltage limits without needing to reinforce the network, while not over using the storage despite the uncertainty, remains a challenge. Similarly, the EVs themselves have been suggested as a flexible load however realising this flexibility also remains a challenge. This Thesis researches the ability of ESSs and EVs to mitigate load and generation uncertainty within urban microgrids. Initially, the technical and economic impacts of uncontrolled EV charging on distribution networks is investigated by combining an extensive real world dataset of EV charging events and domestic household load. It is found that distribution transformer power flow limits will be the first operational limit to be breached when EV penetration reaches 40%. The resulting reinforcement cost that Ofgem would allow Distribution Network Operators (DNOs) to recover from consumers is estimated at £60.81bn - £74.27bn up to 2040. A methodology is then proposed to forecast future uncontrolled EV charging load based on the ‘here and now’ load experienced on the network. In addition, a methodology is proposed to aggregate a number of smart charging EVs to form a Virtual Energy Storage System (VESS) able to deliver services to the distribution network with a high degree of controllability (~99%), while also guaranteeing the energy required by the EVs for their primary purpose of transportation. The VESS is combined with other forms of flexibility to deliver an Enhanced Frequency Response (EFR) service where a fuzzy logic control methodology is proposed to maximise power availability. Finally, a Robust Optimisation (RO) formulation is developed that balances the trade-off between the cost of protecting network operational limits from load and generation uncertainty, against the cost of failing to protect network operational limits. RO requires a linear representation of the power system, and the errors introduced through linearization via sensitivity factors are calculated as up to 1.6% when there is no load and generation uncertainty, and up to 4.0% when there is load and generation uncertainty.Engineering and Physical Sciences Research Council (EPSRC) Siemen