Demand Response Strategy Research Articles

A coordinated management scheme for power quality and load consumption improvement in smart grids based on sustainable energy exchange based model

This paper presents a contrived framework that accompanies the power quality improvement (PQI) and demand-side management (DSM) in the case of recurring voltage-related problems. The illustrative implementation of the proposed framework necessitates two antecedents. First, a comprehensive mathematical formulation of the electrical power for a dynamic voltage restorer (DVR) is presented. Second, a model is developed to analyze the energy exchange during the voltage disturbances. Afterwards, the performance of a DVR system is simulated and a scrutinization is accomplished about the effect of power exchanged by a DVR with distribution system on the load factor. Meanwhile, a modified mathematical model of demand response (DR) is developed to derive the different DR methods with the purpose of load profile improvement (LPI). The simulation results reveal that the DVR operation may decrease the load factor from +3.56% to −3.20% of the usual load factor. Furthermore, in some situations, the DR outcomes might be obliterated by PQI. The implementation of proposed framework demonstrates the load factor can be enhanced up to 7.46% by proper DR strategy adoption.

Optimal management of multi stakeholder integrated energy system considering dual incentive demand response and carbon trading mechanism

Numerous emerging market organizations have entered the energy market as a result of the gradual expansion in energy market reforms, resulting in fierce competition among them. Meanwhile, the deterioration of environmental contamination and rising energy demands have steadily gained prominence. Therefore, to solve the problems of environmental pollution and conflict of interests among multiple stakeholders in the integrated energy system (IES), this paper proposes a novel collaborative optimization strategy for a low-carbon economy in the IES based on carbon trading mechanism and Stackelberg game theory. First, a novel multistakeholder low-carbon transaction mechanism is proposed, which takes into account the initiative and decision-making capacities in the three-party stakeholders of energy supply, demand, and storage. Second, a reward and punishment ladder-type carbon trading mechanism and an integrated demand response strategy based on the price information and carbon compensation are proposed to improve the system economy and environmental benefits. Finally, the mathematical models of each stakeholder are established and are solved by a two-stage optimization algorithm. The simulation results verify that in a carbon-constrained environment, all stakeholders can benefit from the proposed transaction mechanism, resulting in an economical and environmentally friendly optimal scheduling of the IES. Novelty Statement A novel low-carbon transaction mechanism based on the carbon trading mechanism and the Stackelberg game theory is established. A reward and punishment ladder-type carbon trading mechanism is introduced. A novel integrated demand response strategy based on the price information and carbon compensation is proposed. A multistakeholder mathematical optimization model is established and solved using a two-stage algorithm.

Optimal Energy Management of a Campus Microgrid Considering Financial and Economic Analysis with Demand Response Strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

The role of demand response in residential electricity load reduction using appliance shifting techniques

PurposeSome of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa.Design/methodology/approachThe research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior.FindingsThis study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions.Research limitations/implicationsIt should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households.Practical/implicationsLoad shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers.Originality/valueThis paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy

In this paper, a risk-constrained stochastic mixed-integer linear programming (MILP) model is proposed for optimal bidding strategy of a grid-connected CHP-based multi-microgrid (MMG) system in energy and reserve markets considering environmental restrictions. A reward-based demand response program is considered in the proposed framework to realize demand-side management and boost the economic performance of the MMG system. Besides, conditional value-at-risk as a proper risk-averse index is incorporated into the developed mixed-integer programming formulation to hedge the risk of the low profits associated with worst scenarios. For this study, the uncertainty of energy market price, electrical load, and power generation of renewable energy sources are taken into account through employing stochastic programming. Meanwhile, given the global policy for emissions reduction, an emission constraint is considered into the model, ensuring a more green trading strategy. The obtained simulation results over a 24-h time horizon, demonstrate that increasing the risk factor leads to the profit decrement by almost 30.37% while the conservativeness of the MMG against the risk is improved. Also, the results reveal that by increasing the value of emission factor, the system's profit is decreased by about 30.03% while the MMG would be operated more environmental-friendly.

Two-stage flexible power sales optimization for electricity retailers considering demand response strategies of multi-type users

Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model.

A Temperature & Humidity Setback Demand Response Strategy for HVAC Systems

• A T&H demand response strategy that adjusts the temperature & humidity is proposed • Indoor air, supply air and chilled water temperatures are adjusted in the strategy • The energy saving and the peak shedding of the strategy are up to 39.0% and 37.1% • The T&H strategy increases the energy saving by 22.9% compared with the T strategy • The strategy is more effective under lower sensible load or higher latent load Heating, ventilation and air conditioning (HVAC) system plays a significant role in demand response (DR) to reduce the peak load of the grid. Conventionally, only the sensible load is reduced by increasing the indoor temperature during DR periods and the latent load is rarely concerned. This study proposes a temperature & humidity (T&H) setback strategy for HVAC systems to reduce both the sensible and latent cooling loads by adjusting the supply air temperature and chilled water supply temperature simultaneously. The performance of the T&H setback strategy is analyzed from the perspectives of peak shedding, energy saving and indoor thermal comfort and compared with the common temperature setback strategy. The impact of weather conditions and occupant densities on the performance of T&H strategy is assessed. Results show that the energy saving of the HVAC system under the T&H setback strategy can be up to 39.0% and peak shedding can be increased to 37.1%, which are both higher compared with the temperature setback strategy. The peak shedding and energy saving of the T&H setback strategy is larger when the buildings have lower sensible or higher latent load. The T&H strategy can improve the flexibility of buildings during DR periods effectively.

Arranging university semester date to minimize annual CO2 emission: A UK university case study

SummaryExisting methods of reducing carbon emissions on campus often require substantial investment, and the potential opportunities for carbon dioxide and energy savings in universities with existing infrastructure have not been considered in much detail. This work fills this gap by considering an indirect and soft demand response strategy, i.e., semester arrangement. To identify the optimal operational strategy of a realistic campus-level multi vector energy system (MES) in Scotland based on CO2 emissions, an original tool is presented. Two conclusions can be drawn safely from the case study. Firstly, changing the operational mode of the university could significantly reduce CO2 emissions. Secondly, considering the difference between average emission factor (AEF) and marginal emission factor (MEF) in the power grid, the different operational modes will bring different electricity/heat demands and also affect carbon emissions. The work opens up a new perspective for worldwide university operators who are considering reducing CO2 emissions.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%.

Time of use electricity pricing in power system planning and operation: Case study of Nepalese power system

The difference between load demand in the off-peak and peak period is usually high in the context of Nepal, and the high peak load for a short time is the main reason for insufficiency in electricity, frequent electricity outage, and poor power quality. This paper investigates the effectiveness of the time of use (TOU) strategy to reduce peak demand and to improve power quality. TOU electricity price is one of the electric demand response (DR) strategies, which may motivate the customers to reduce their consumption in peak periods and shift load in the off-peak period, which propel the electricity industry towards a higher efficiency compared to that of common flat prices. In this paper, a process based on an equal step length iteration algorithm is used to obtain an optimal period partition. Using the particle swarm optimization (PSO) algorithm, a TOU-based optimization model is proposed to find the optimal electricity price. Then, its impact on peak demand reduction and voltage fluctuation is analyzed. The impact of TOU price on peak demand reduction is investigated in the hourly load profile of Nepal, which indicates the significant reduction in peak demand. Similarly, the impact on voltage fluctuation is analyzed through a case study of the IEEE 33-bus radial distribution test system, which shows voltage profile is improved.

Operation of Distributed Battery Considering Demand Response Using Deep Reinforcement Learning in Grid Edge Control

Battery energy storage systems (BESSs) are able to facilitate economical operation of the grid through demand response (DR), and are regarded as the most significant DR resource. Among them, distributed BESS integrating home photovoltaics (PV) have developed rapidly, and account for nearly 40% of newly installed capacity. However, the use scenarios and use efficiency of distributed BESS are far from sufficient to be able to utilize the potential loads and overcome uncertainties caused by disorderly operation. In this paper, the low-voltage transformer-powered area (LVTPA) is firstly defined, and then a DR grid edge controller was implemented based on deep reinforcement learning to maximize the total DR benefits and promote three-phase balance in the LVTPA. The proposed DR problem is formulated as a Markov decision process (MDP). In addition, the deep deterministic policy gradient (DDPG) algorithm is applied to train the controller in order to learn the optimal DR strategy. Additionally, a life cycle cost model of the BESS is established and implemented in the DR scheme to measure the income. The numerical results, compared to deep Q learning and model-based methods, demonstrate the effectiveness and validity of the proposed method.

A multistage distribution network planning method considering distributed generation active management and demand response

This paper proposes a multistage distribution network planning (DNP) method that considers multiple planning alternatives, the active management of distributed generation (DG) units and demand response in the context of active distribution networks (ADNs). Representative days are selected for the consideration of uncertainties related with load demand and renewable power generation, during the planning period. The proposed DNP method aims at minimising the net present value of the total investment and operational cost. The proposed DNP considers multiple planning alternatives, such as the reinforcement of existing substations and existing distribution lines, the installation of new distribution lines, and the installation of capacitors. Furthermore, the proposed DNP method incorporates the control capabilities of DG active and reactive power output and demand response schemes. A 24-bus distribution network and a 144-bus real world distribution network are used to validate the performance of the proposed method.

Integrated Transportation, Building, and Electricity System Models to Explore Decarbonization Pathways in Regina, Saskatchewan

In Canada, the majority of urban energy demand services the transportation or building sectors, primarily with non-renewable energy sources including gasoline and natural gas. As a result, these two sectors account for 70% of urban greenhouse gas (GHG) emissions. The objective of this paper is to explore the potential for co-benefits when simultaneously electrifying transportation and building demand sectors while expanding variable renewable energy (VRE) production. The investigation uses a novel integrated framework of the transportation, building, and electricity sectors to represent the operational implications of demand side flexibility on both the demand and supply side of the energy system. This original approach allows for very fine temporal and spatial resolution within models, while still performing a multi-sector analysis. First, the activity-based transportation model produces passenger travel demand profiles, allowing for investigation of potential electricity demand and demand response from electric vehicles with high spatial and temporal resolution. Second, the archetype-based building model predicts electricity demand of the residential building sector, allowing for investigation into demand-side management strategies such as load-shifting, building retrofits, and changes in appliance technology. Third, the electricity system production cost dispatch model is used to model the operations of Regina's electricity grid and has a spatial resolution capable of assessing individual and connected positive energy districts as well as VRE integration. Through linking of these three models, the effects of consumer flexibility in transportation and building energy demand are explored, especially in the context of introducing much needed flexibility for large-scale VRE integration. A utility-controlled demand response (DR) strategy is explored as means for Regina to reach their renewable target, along with battery storage. Various pathways to Regina's target are considered, based on the various proposed scopes of the target. The results show that Regina can meet their renewable target with large-scale rooftop solar and wind capacity. DR strategies are marginally effective in aiding toward the renewable target, but, when implemented in conjunction with battery storage, is able to get Regina to within 1% of their renewable target.

Simultaneous analysis of frequency and voltage control of the interconnected hybrid power system in presence of FACTS devices and demand response scheme

This work confers the simultaneous analysis of voltage and frequency control of the 3-area interconnected hybrid power system (IHPS) consisting of parabolic-trough solar power system (PSP), wind power system (WPS) and dish-stirling solar power system (DSP) under the paradigm of microgrid. The speculated result of the IHPS is presented and analyzed considering real and reactive power as the function of both voltage and frequency. 9The proposed IHPS under investigation has been mathematically modeled for direct coupling like active power–frequency and reactive power–voltage relationships and cross coupling like active power–voltage and reactive power–frequency​ relationships. The system responses under different operating conditions have been investigated to see the cross-coupling behavior of the proposed IHPS in the presence of voltage compensating devices like dynamic voltage restorer (DVR) and Static Synchronous Compensator (STATCOM). Further, Demand Response Scheme (DRS) as a frequency control strategy has been considered to enhance the system stability. System responses have been critically analyzed under Mine Blast Algorithm (MBA) based proportional–integral–derivative (PID) controllers

Towards low-carbon district heating: Investigating the socio-technical challenges of the urban energy transition

District heating is a major energy infrastructure in many urban settlements in the world, contributing significantly to greenhouse gas emissions. Decarbonising district heating is an important step towards the realisation of a carbon-neutral society that entails considerable socio-technical change. Building on sustainability transitions literature that has dealt with socio-technical reconfiguration, this paper investigates the barriers to the implementation of a low-carbon district heating system that is based on biomass incineration minimisation and the total phasing out of fossil fuels. Empirically, the study relies on an extensive stakeholder analysis that involved 44 organisations representing technology providers, energy companies, industry organisations, policymakers, local authorities and researchers. The results show that while several stakeholder groups could converge on key issues such as the need to support certain technological niches and the danger of a biomass lock-in, divergences regarding barriers to be removed existed between policymakers, new entrant firms, and building owners. Cities were considered important actors for the implementation of the proposed low-carbon district heating concept. However, they should encourage building owners' participation in demand response schemes, decentralized renewable energy production, and the re-design of local electricity networks to support district heating electrification.

A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

New energy generation forecasting and dispatching method based on big data

In order to realize the optimal and reasonable distribution of the load of the power system and improve the ability of the power system to consume new energy generation, this paper analyzes the operation big data of photovoltaic power plants, wind farms and thermal power units with the example of a region. Based on this, a predictive scheduling method of new energy generation based on big data is proposed. By constructing a model of new energy output and power load, the method is able to predict the dispatch value of wind power, PV and power load. Finally, through the simulation analysis, the results show that the proposed method can deeply tap the demand response potential of the integrated energy system, provide strong data support for the formulation of a reasonable integrated demand response scheme, and has strong practical application value.

Response reliability and risk analysis of users under time‐of‐use price

Time-of-use (TOU) electricity prices are implemented based on the response characteristics of users. TOU prices guide the electricity consumption behaviour of users and make them actively respond to peak shaving and valley filling. In this paper, the conventional psychology response model of consumers is improved based on the history data of customer response to the TOU price. The paper defines four types of response load curves and proposes the response reliability indices. Additionally, it establishes the probability models of customer response. According to the characteristics of customer response, the sensitivity analysis of influencing factors and the risk analysis of customers are carried out according to the probability model of response. For instance, risk and characteristics analyses of the customer response were performed using the load data of residential areas in Nanjing city. The results indicate that the probability distribution of the customer response is approximate to the trapezoid distribution. The sequence of sensitivity coefficients of customer response factors is as follows: customer income, type of customer, and electrical equipment. The proposed method provides a scientific basis for electricity price implementers to accurately grasp the response behaviour of the user and establish a reasonable demand response strategy.

A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia

The use of hybrid renewable energy systems (HRES) has become the best option for supplying electricity to sites remote from the central power system because of its sustainability, environmental friendliness, and its low cost of energy compared to many conventional sources such as diesel generators. Due to the intermittent nature of renewable energy resources, there is a need however for an energy storage system (ESS) to store the surplus energy and feed the energy deficit. Most renewable sources used battery storage systems (BSS), a green hydrogen storage system (GHSS), and a diesel generator as a backup for these sources. Batteries are very expensive and have a very short lifetime, and GHSS have a very expensive initial cost and many security issues. In this paper, a system consisting of wind turbines and a photovoltaic (PV) array with a pumped hydro energy storage (PHES) system as the main energy storage to replace the expensive and short lifetime batteries is proposed. The proposed system is built to feed a remote area called Dumah Aljandal in the north of Saudi Arabia. A smart grid is used via a novel demand response strategy (DRS) with a dynamic tariff to reduce the size of the components and it reduces the cost of energy compared to a flat tariff. The use of the PHES with smart DRS reduced the cost of energy by 34.2%, and 41.1% compared to the use of BSS and GHSS as an ESS, respectively. Moreover, the use of 100% green energy sources will avoid the emission of an estimated 2.5 million tons of greenhouse gases every year. The proposed system will use a novel optimization algorithm called the gradually reduced particles of particle swarm optimization (GRP-PSO) algorithm to enhance the exploration and exploitation during the searching iterations. The GRP-PSO reduces the convergence time to 58% compared to the average convergence time of 10 optimization algorithms used for comparison. A sensitivity analysis study is introduced in this paper in which the effect of ±20% change in wind speed and solar irradiance are selected and the system showed a low effect of these resources on the Levelized cost of energy of the HRES. These outstanding results proved the superiority of using a pumped-storage system with a dynamic tariff demand response strategy compared to the other energy storage systems with flat-rate tariffs.

Simultaneous Long-Term Planning of Flexible Electric Vehicle Photovoltaic Charging Stations in Terms of Load Response and Technical and Economic Indicators

Photovoltaic charging stations (PVCSs) are one of the most important pieces of charging equipment for electric vehicles (EVs). Recently, the process of designing solar charging stations as flexible sources has been growing and developing. This paper presents a relatively complete design of a solar charging station as a flexible economic resource in a 10-year planning horizon based on a genetic algorithm in two scenarios. PVCSs are not considered in the first scenario. This scenario is only to confirm the results, and the proposed method is proposed. However, in the second scenario, the effects of PVCSs and the demand response strategy (DR) on this development are considered. Copula probability distribution functions are used to create appropriate scenarios for vehicles during different planning years. The proposed energy management system shows a stable performance in terms of the annual load growth index and electricity price of each level of demand over the time horizon along with minimizing power losses and costs required, which makes PVCS efficiency higher and gives them a suitable structure and stability. The modeling results in terms of uncertainties in the system indicate that the use of load management along with PVCS design and flexible electric vehicle charge control strategies improves power quality parameters and optimizes system cost over a period of 10 years. Compared to the obtained results with the traditional case, it is observed that long-term planning in terms of DR and PVCSs and the technical specifications of the network have been improved. As a result of this proposed long-term planning, PVCSs are more flexible.