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EV charging cost versus total daily EV energy demand for all buildings for the 722 06:30-19:30 hours layover for the entire year 2019 for initial and final SOC of 50 & 90% 723 respectively for (a) V0G charging, (b) V1G charging, and (c) V2B charging. The legend 724 represents the building number. 725
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Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs in commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimiz...
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... initial 680 and final SOC combination is chosen as 50 & 90% respectively to be consistent with the rest of 681 the paper. 682 Figure 6 shows that for all buildings, V0G incurs the highest EV charging costs (the 683 difference between post and pre-EV charging building electricity costs), followed by V1G and 684 V2B. For V0G charging, all charging takes place between 06:30-10:15 hours (see Section 3.2.1). ...
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... 11 The application and development of EV battery bidirectional transmission technology have transformed these challenges into opportunities. 12 By implementing orderly charging and discharging control of EVs, it is possible to achieve peak-shaving and valley-filling for the building microgrid's load and to enhance the consumption of photovoltaicgenerated electricity for EV charging. ...
The application of vehicle-to-building (V2B) technology to integrate photovoltaic charging stations (PVCS) with smart building microgrids has gradually emerged as a new low-carbon operation model in the electric vehicle (EV) energy supply industry. The disordered integration of a large number of EVs into the power grid has caused numerous safety issues for building microgrids. Considering that buildings suitable for the construction of PVCS are primarily concentrated in residential, office, and commercial areas, this study proposes an optimized scheduling strategy for the charging and discharging of electric vehicles that considers different types of buildings. First, a PV-building-EV integrated system based on V2B is established. Probability distribution models of EV travel characteristics for different regions are obtained through actual data fitting, and unordered charging load curves for each region are derived by incorporating the Monte Carlo sampling method. On this basis, considering the interest of the building microgrids, PVCS operators, and EV users, a multi-objective optimization scheduling model for orderly EV charging and discharging based on V2B is established. Finally, the improved Non-dominated Sorting Genetic Algorithm II validates the model's effectiveness.
... Reference In the US and other parts of the world, the electricity tariff for most commercial consumers includes a monthly demand charge, which is calculated based on the highest average load requested from the grid, measured in kW, within a 15minute interval of the monthly billing period [1]. Demand charge rates are usually one to two orders of magnitude higher than energy charge rates [2]- [4]. Thus, demand charge management (DCM) based on economic cost minimization is attractive in sizing, day-ahead planning and real-time operation of microgrids. ...
Monthly demand charges form a significant portion of the electric bill for microgrids with variable renewable energy generation. A battery energy storage system (BESS) is commonly used to manage these demand charges. Economic model predictive control (EMPC) with a reference trajectory can be used to dispatch the BESS to optimize the microgrid operating cost. Since demand charges are incurred monthly, EMPC requires a full-month reference trajectory for asymptotic stability guarantees that result in optimal operating costs. However, a full-month reference trajectory is unrealistic from a renewable generation forecast perspective. Therefore, to construct a practical EMPC with a reference trajectory, an EMPC formulation considering both non-coincident demand and on-peak demand charges is designed in this work for 24 to 48 h prediction horizons. The corresponding reference trajectory is computed at each EMPC step by solving an optimal control problem over 24 to 48 h reference (trajectory) horizon. Furthermore, BESS state of charge regulation constraints are incorporated to guarantee the BESS energy level in the long term. Multiple reference and prediction horizon lengths are compared for both shrinking and rolling horizons with real-world data. The proposed EMPC with 48 h rolling reference and prediction horizons outperforms the traditional EMPC benchmark with a 2% reduction in the annual cost, proving its economic benefits.
... In fact, these are deemed energy consumption external to the building's physical boundaries, and they are considered an additional energy carrier. Furthermore, vehicle consumption is related to the number of vehicles and the extent of travel, which may vary considerably for different users [35,36]. Lastly, even though an e-vehicle charge can provide flexible options to the building, an in-depth investigation of charging behaviour patterns and the overall charging infrastructure is needed. ...
Purpose: Plus Energy Buildings (PEBs) are gaining attention in construction for providing advantages not only on the singular building but also on higher levels, namely neighbourhoods and national grids. Different from the current standard of net zero energy buildings, PEBs lack broader and holistic investigations that consider energy, environmental, and economic performance indicators. These are needed to evaluate their often-debated effectiveness and environmental sustainability. To this purpose, this work assesses technical equipment functional systems aimed at PEBs by considering energy, environmental and cost performance indicators and by carrying out a multi-case and multi-domain investigation.
Method: A parametric modelling is carried out based on building energy simulations and user energy profile modelling in 16 case studies. Relevant Key Performance Indicators are derived and followed to attempt result clustering and to derive general considerations for the designed solution.
Finding: The study showed that the effectiveness of such systems is limited if PV modules are located on the roof exclusively. Moreover, heat pumps and PV technologies need to be better coupled and harmonised in subarctic regions. Overall, centralised systems perform better, and environmental and economic advantages depend on the national energy and economic context. Such results can be considered valid under the same conditions and circumstances; therefore, an extension of case studies is needed.
... The objective function is formulated as in [18], [34], and is given by, ...
Chance constrained stochastic model predictive controllers (CC-SMPC) trade off full constraint satisfaction for economical plant performance under uncertainty. Previous CC-SMPC works are over-conservative in constraint violations leading to worse economic performance. Other past works require a-priori information about the uncertainty set, limiting their application to real-world systems. This paper considers a discrete linear time invariant system with hard constraints on inputs and chance constraints on states, with unknown uncertainty distribution, statistics, or samples. This work proposes a novel adaptive online update rule to relax the state constraints based on the time-average of past constraint violations, for the SMPC to achieve reduced conservativeness in closed-loop. Under an ideal control policy assumption, it is proven that the time-average of constraint violations converges to the maximum allowed violation probability. The time-average of constraint violations is also proven to asymptotically converge even without the simplifying assumptions. The proposed method is applied to the optimal battery energy storage system (BESS) dispatch in a grid connected microgrid with PV generation and load demand with chance constraints on BESS state-of-charge (SOC). Realistic simulations show the superior electricity cost saving potential of the proposed method as compared to the traditional MPC (with hard constraints on BESS SOC), by satisfying the chance constraints non-conservatively in closed-loop, thereby effectively trading off increased cost savings with minimal adverse effects on BESS lifetime.
... The utility tariffs are as follows: c NCD , c PD , c E [23], and c EV are USD 24.48/kW and USD 28.92/kW in summer; USD 19.23/kW in winter; USD 0.107/kWh for off-peak hours and USD 0.126/kWh for peak hours; and USD 0.150/kWh, respectively. The threshold, h NCD (h PD ), is the highest actual EV load up to time t (during peak demand hours) of the month. ...
In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.
... In accordance with the prescribed regulations governing EV involvement in energy management, the charging expense for every EV is computed using Eqs 1, 2, which can be utilized to compute the energy cost of all EVs when multiple EVs are engaged in energy management (Ghosh et al., 2022). ...
The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.
... In accordance with the prescribed regulations governing EV involvement in energy management, the charging expense for every EV is computed using Eqs 1, 2, which can be utilized to compute the energy cost of all EVs when multiple EVs are engaged in energy management (Ghosh et al., 2022). ...
The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study's significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm's adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing. KEYWORDS smart home, electricity energy, energy management, electric vehicle, time of use, inclining block rate OPEN ACCESS EDITED BY CITATION Dodo YA, Ibrahim AO, Abuhussain MA, Baba Girei ZJ, Maghrabi A and Naibi AU (2024), An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity.
... where N 2 is the number of EV users that did not choose CS1 276 in the initial plan without carbon revenue. the charging price, and the total number N β 2 is formulated as 285 N β 2 = (γ + ω wait )N 2wait (9) where N 2wait is the total number of waiting users of all CSs 286 exceeding the waiting threshold. ...
With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profits improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profits optimization of the dynamic charging price model by particle swarm optimization algorithm (PSO). Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods.
... The forecasting methodology for AT, PD and ED is described in the next section. The problem formulation in this work follows [25] with the SOC formulation in [25] adjusted to the ED formulation. The MPC problem is solved with a finite time horizon with control input only applied to the EVs that are plugged in at the current time step. ...
... The forecasting methodology for AT, PD and ED is described in the next section. The problem formulation in this work follows [25] with the SOC formulation in [25] adjusted to the ED formulation. The MPC problem is solved with a finite time horizon with control input only applied to the EVs that are plugged in at the current time step. ...
... 1 Other charges consist of the DWR bond charge ($0.00580× total energy usage in a month), the City of San Diego Franchise fee ($0.0578 × {rncγnc(k) + ropγop(k) + ∆t N j=k rec(j)L(j)}), the DWR bond franchise fee ($0.0688×DWR bond charge), the CA State Surcharge ($0.00030×total energy usage in a month), and the CA state regulatory charge ($0.00058×total energy usage in a month) [25]. ...
This work proposes a novel EV forecasting technique that predicts each EV’s arrival time (AT), energy demand (ED) and plug duration (PD) over the course of a calendar day using a hybrid machine learning (ML) forecast. The ML forecasts as well as persistence forecasts are then input in a model predictive control (MPC) algorithm that minimizes the electricity costs incurred by the charging provider. The MPC with the hybrid ML forecast reduced peak loads and monthly electricity costs over a base case scenario that determined costs for uncontrolled L2 charging: Reductions in weekday mean peak load during a 30 day summer time case study were 47.0% and 3.3% from the base case to ML MPC and persistence to ML MPC, respectively. Reductions in utility costs during the summer case study were 22.0% and 1.4% from base case to ML MPC and persistence to ML MPC respectively. Results are similar for a 30 day winter case study.
... Electric cars (ECs) play a vital role in reducing carbon emissions and improving sustainable development. 1,2 However, scarce charging infrastructure is recognized as one of the barriers to large-scale adoption of ECs. 3 Only about 30% of drivers' daily trips can be strictly satisfied by an existing affordable 200-mile EC through overnight charging at home. 4 Moreover, some users suffer inconvenient home charging as the progress of private charging stations is far behind. Many owners are thus forced to carefully consider the use of ECs owing to the strong demand for recharging. ...
... Linearization is employed to reduce the complexity. In the MINLP, the non-linear constraints terms are constraints (2) and (15). Constraint (2) is a maximum value form. ...
Electric cars (ECs) play a vital role in reducing carbon emissions and improving sustainable development. Due to the lack of public charging facilities, the application of private ECs is limited. Motivated by the sharing economy, this paper is the first paper to investigate the idea of sharing electric bus (EB) charging stations to alleviate the plight of private EC owners. However, existing EB charging strategies do not cater to the sharing and hinder the implementation of the policy. Therefore, this paper proposed a general mathematic mixed-integer non-linear programming (MINLP) model to coordinate the optimal charging strategy and sharing operation to meet the external and internal goals of bus systems, namely maximizing charging station availability and satisfying the EB charging demand. Harnessing real-world bus operating data, the proposed model effectively generates multiple charging strategies. The results show that some hybrid strategies can achieve a similar sharing availability period as the public-oriented strategy with lower costs. Besides, sensitivity analysis indicates that increasing battery capacity can extend the sharing availability period while increasing charging power has only a minor impact. Overall, the proposed model obtains desired charging scheduling and provides concrete suggestions to promote the sustainable development of ECs and EBs.
