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Pricing Strategy of PV-Storage-Charging Station Considering Two-Stage Market Bidding
... The main reason for overlooking data services is the privacy concerns that can arise from sharing private data. Revenue maximization based on charging fees can be done using ToU pricing, implementing demand response programs, and various pricing schemes discussed before [63,118,119]. Besides bidirectional charging, other ancillary services, like voltage droop control, active power transfer [120], and flexibility services for power systems [121], can maximize the revenue of EVCSs (Fig. 3). ...
The growing adoption of Electric Vehicles (EVs) presents pressing challenges and opportunities for power systems and market dynamics. This paper comprehensively reviews state-of-the-art operational optimization techniques for EV charging, including model predictive control, reinforcement learning, and distributed approaches. The study examines strategies to address uncertainties in renewable generation, market pricing, and EV charging behavior. Advanced pricing schemes like distribution locational marginal pricing and game-based methods are explored to align profitability with system stability. The significance of bidirectional charging in reducing peak loads, supporting ancillary services, and balancing battery degradation with user satisfaction is also discussed. Finally, emerging challenges in privacy, multi-energy coupling, and regulation are presented, emphasizing research directions to enhance grid resilience, economic viability, and sustainability. This review offers valuable insights for policymakers, energy utilities, and EV stakeholders to facilitate a smooth and cost-effective transition toward an electrified and decarbonized transportation and power sector.
This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.
Electric Vehicle (EV) charging stations will play an important role in the smart city. Uncoordinated and statistical EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. In this paper, a hybrid optimization algorithm for energy storage management is proposed, which shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of EV charging stations. The algorithm comprises of three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data and optimization for minimizing the operating cost of EV charging station integrated with PV and ESS. Extensive simulation study is carried out with an
uncoordinated and statistical EV charging model in the context of Singapore to check effectiveness of this algorithm. Furthermore, detailed analysis of subsidy and incentive to be given by the government agencies for higher penetration of renewable energy is also presented. This work would aid in planning of adoption of PV integrated EV charging stations which would expectedly replace traditional gas stations in future.
This paper proposes a novel electric vehicle (EV) classification scheme for a
photovoltaic (PV) powered EV charging station (CS) that reduces the effect of
intermittency of electricity supply as well as reducing the cost of energy
trading of the CS. Since not all EV drivers would like to be environmentally
friendly, all vehicles in the CS are divided into three categories: 1) premium,
2) conservative, and 3) green, according to their charging behavior. Premium
and conservative EVs are considered to be interested only in charging their
batteries, with noticeably higher rate of charging for premium EVs. Green
vehicles are more environmentally friendly, and thus assist the CS to reduce
its cost of energy trading by allowing the CS to use their batteries as
distributed storage. A different charging scheme is proposed for each type of
EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed
integer programming (MIP) technique is used to facilitate the proposed
classification scheme. It is shown that the uncertainty in PV generation can be
effectively compensated, along with minimization of total cost of energy
trading to the CS, by consolidating more green EVs. Real solar and pricing data
are used for performance analysis of the system. It is demonstrated that the
total cost to the CS reduces considerably as the percentage of green vehicles
increases, and also that the contributions of green EVs in winter are greater
than those in summer.
The promotion of distributed generation (DG) is a direct consequence of an international drive for sustainable development and advances in technology. At present, the development of environmentally friendly (renewable energy sources) and high efficiency power generation (combined heat and power) has attracted significant attention all over the world. However, traditional supervision and management systems in the power industry prevent a full understanding of the benefits of DG, especially those impacting on the environment, by the policy maker and policy executor. This has therefore, hindered the realisation of the full environmental benefits of DG. In this paper, some indices are introduced to quantitatively analyse the value of the environmental benefits of DG in China. In addition, a new mathematical model for calculating the true generation cost of DG was established based on the environmental benefits indices. In this model, a penalty mechanism was introduced for the pollutant emissions of power generation and the environmental value of pollutant emissions was also considered. Simulation results show that the indices and the new model can correctly reflect the environmental benefits and their application should accelerate the development of DG in China.
The concept of peer-to-peer (P2P) trading, or transactive energy (TE), is gaining momentum as a future grid restructure. It has the potentials to utilize distributed energy resources (DERs), proactive demand side management (DSM), and the infusion in information and communication technologies (e.g., blockchain and internet of things (IoT)) for promoting the technical and economic efficiency of the system in its entirety. An efficient market framework is vital for the successful and sustainable implementation of such a concept. This paper proposes a P2P energy trading framework enabled by blockchain. It consolidates bilateral contracts, an electronic-commerce platform, a double-auction Vickrey-Clarke-Groves (VCG) mechanism, and trading functionalities with the main grid. Through these multi-layer mechanisms, various trading preferences and attributes of electricity generation and/or consumption are accommodated. Meanwhile, the VCG mechanism eliminates any potential for market power exercise via incentivizing truthful bidding of participants. Different remedies are proposed to overcome the drawback of VCG, i.e., the lack of balanced-budget property. Accordingly, the proposed trading framework is described as multi-settlement and quasi-ideal. Case studies are conducted to analyze and evaluate the proposed trading framework and demonstrate the effectiveness of the proposed remedies in handling probable market deficiencies.
In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due to the significant uncertainty associated with local generation. This paper proposes a peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously. The novelty is to match the forecast power with demand having time flexibility and the uncertain power with demand having power flexibility. This market enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system. In the test case, 55.3% of PV forecast error can be balanced locally in the proposed joint market. In comparison, 43.6% of PV forecast error is balanced locally when the forecast power and uncertain power are traded separately in a day-ahead market and a real-time market. The proposed P2P market can also motivate PV owners to improve forecast accuracy.
Currently, a lot of distributed generators and household electric vehicles are connected to the grid. The increase in capacity and the variety of loads have made it impossible to support the new load demand by the original microgrid. It is imperative to establish a new microgrid system that can meet a variety of needs. Taking into account the comprehensive constraints and the orderly charging, this paper develops a multi-objective optimal planning model in AC/DC hybrid microgrid system with wind turbine generators, photovoltaic generation, energy storage battery and electric vehicle. In order to reduce the cost of configuration and the load fluctuation, CPLEX optimal programming has been used to solve the objective function. The case study compares the difference between the new AC/DC hybrid microgrid and the original microgrid. A study is made on the rationality of electric vehicle orderly charging as well as the influences of interaction between the two types of microcrids on optimized configuration. The results verify the effectiveness and feasibility of the approach proposed.
This paper focuses on the self-scheduling problem of an aggregator of plug-in electric vehicles (PEVs) purchasing energy in the day-ahead market, and offering balancing services for a wind power producer, i.e., committing to compensate the forecast errors of wind power plants. The aggregated charging and discharging flexibility of the PEV fleet is represented by a probabilistic virtual battery model, accounting for the uncertainty in the driving patterns of PEVs. Another source of uncertainty is related to the balancing requests, which are a function of the forecasted wind power output. A scenario-based robust approach is used to tackle both sources of uncertainty in a tractable way. The interdependency between the day-ahead market prices and the aggregator’s bidding decisions is addressed using complementarity models. A case study analyzes the capability of the PEV aggregation to provide balancing services, for different settings of the balancing contract, and both with and without the use of vehicle-to-grid.
This paper proposes a novel approach to energy exchange between electric vehicle (EV) load and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. An optimal bidding/offering strategy model is developed to mitigate wind energy and EV imbalance threats, and optimize EV charging profiles. A new strategy model is based on optimizing decision making of a wind generating company (WGenCO) in selecting the best option among the use of the balancing or regulation services, the use of the energy storage system (ESS) and the use of all of them to compensate wind power deviation. Energy imbalance is discussed using conventional systems, ESS, and EV-Wind coordination; results are compared and analyzed. Stochastic intra-hour optimization is solved by mixed-integer linear programming (MILP). Uncertainties associated with wind forecasting, energy price, and behavior of EV owners based on their driving patterns, are considered in the proposed stochastic method and validated through several case studies.
In this paper, we study the price competition among electric vehicle charging stations (EVCSs) with renewable power generators (RPGs). As electric vehicles (EVs) become more popular, a competition among EVCSs to attract EVs is inevitable. Thereby, each EVCS sets its electricity price to maximize its revenue by taking into account the competition with neighboring EVCSs. We analyze the competitive interactions between EVCSs using game theory, where relevant physical constraints such as the transmission line capacity, the distance between EV and EVCS, and the number of charging outlets at the EVCSs are taken into account. We show that the game played by EVCSs is a supermodular game and there exists a unique pure Nash equilibrium for best response algorithms with arbitrary initial policy. The electricity price and the revenue of EVCSs are evaluated via simulations, which reveal the benefits of having RPGs at the EVCSs.
Electric buses (EBs) are undergoing rapid development because of their environmental friendliness. Different from private electric vehicles, EBs are scheduled by a public transport company and required to be charged as soon as possible during operation hours. Consequently, the implementation of fast charging stations (FCSs) is essential to support the operation of EBs. Usually, the size of an FCS is constrained by the residual capacity of the connected distribution network and the investment budget. An energy storage system (ESS) is considered as a potential supplement not only to reduce the network integration cost for FCSs but also to reduce the charging cost of EBs through electricity price arbitrage. To quantify the value of ESS in an electric bus FCS, a mixed integer nonlinear programming (MINLP) formulation for optimal sizing and control of FCS and ESS is built. A simplification method is proposed to convert the MINLP problem to a linear one without any loss in optimality, which accelerates the solving process. Simulations are performed based on historical data of a practical FCS. Numerical results indicate that ESS can significantly help reduce the overall investment and charging cost of the FCS. Meanwhile, the parameters of investment cost, lifespan and time-of-use electricity price have significant influences on the overall value of ESS for an FCS.
This paper presents a new approach implemented in MISO day-ahead and real-time energy and ancillary service market clearing processes to address the reserve deliverability issue. In this approach, MISO enhanced the co-optimization process to incorporate post zonal reserve deployment transmission constraints. Reserves are procured within the co-optimization to meet both market-wide reserve requirements and ensure their deliverability on a zonal basis. As a result, zonal market clearing prices (MCPs) for reserves can properly reflect the effects of zonal reserve deployment on transmission constraints under consideration.
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