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This paper presents a novel DSO support tool with visualisation capability for forecasting network congestion in distribution systems with a high level of renewables. To incorporate the uncertainties in the distribution systems, the probabilistic power flow framework has been utilised. An advanced photovoltaic production forecast based on sky images and a load forecast using an artificial neural network is used as the input to the tool. In addition, advanced load models and operating modes of photovoltaic inverters have been incorporated into the tool. The tool has been applied in case studies to perform congestion forecasts for two real distribution systems to validate its usability and scalability. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and is able to visualise the cumulative probabilities of nodes voltage deviation and network components (branches and transformers) congestion for a variety of forecast horizons as desired by the DSO. The results have also shown that explicit inclusion of load‐voltage dependency models would improve the accuracy of the congestion forecast. For demonstrating the applicability of the tool, it has been integrated into an existing distribution management system via the IoT platform of a DMS vendor, Atos Worldgrid.

In this study, an optimisation model is developed for two-stage energy management of a residential building to minimise energy cost under monthly power-based tariffs for peak demand and time variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimises the energy cost in the second stage. The optimisation problem of the second stage is solved in a rolling time window for the real-time operation of the flexible energy sources in the building. The optimal charging and discharging of the battery energy system, the charging of the electric vehicle battery, the operation of the heating system and the optimal start times of washing machines and dishwashers are determined close to real-time. The proposed approach allows the user to determine the expected peak during the month ahead and try to keep the peak demand in daily operation below that value using a close to real-time energy management system. The performance of the two-stage approach for demand-side management of a residential building has been validated by a realistic case study.

This paper presents the experimental validation of a transmission line protection scheme based on dynamic state estimation for different fault types and conditions. The protection scheme utilizes real-time high-frequency sampled measurements from advanced sensors and evaluates the operating condition of the transmission line based on which a tripping signal is generated in case a fault occurs. The validation is performed using a physical scaled-down model of a power system, consisting of a transmission line, transformer, synchronous generator, and loads. The following faults are examined during the validation: unbalanced faults under different load conditions, high impedance fault, fault current fed from both ends, hidden failure, external fault, and load change conditions. The results show that the scheme performs as intended and thus proves its efficacy to detect various types of faults. The maximum fault detection time is calculated to be 42.5 milliseconds, while the maximum fault clearing time comes out to be 82.5 milliseconds, on par with currently employed protection methods. The obtained results demonstrate the ability of the scheme to detect different fault types under varying conditions and avoid potential issues with relay coordination

This article presents a model for energy management system of a building microgrid coupled with a battery energy storage. The model can be used to dispatch the battery as a flexible energy resource using a market-based setting. The battery is modeled considering battery degradation and real-life operation characteristics derived from measurements at a residential building. The performance of the model was evaluated first with simulations and integrated afterwards to an energy management system, which was demonstrated at a real residential building (HSB Living Lab) equipped with photovoltaic and battery storage systems. The simulation results showed that the building owner, and subsequently the residents, could benefit from the proposed model in reduced annual cost up to 3.1% under the considered pricing scheme. The demonstration results showed that dispatch under the measurement-based model could decrease the undelivered energy over the daily requested amount from the battery from 13.3% to 3.7%. Thus, the proposed model, which couples the measurement-based dispatch with battery degradation, can lead to a more accurate estimation of the building operation cost and an improved overall performance of battery as a flexible resource in building microgrids.

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a grid-connected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab) showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid's total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid's peak power cost as shown by its 20.2% increase in comparison to the ideal case.

This paper presents the experimental validation of a protection scheme for a transmission line based on dynamic state estimation along with the practical application of advanced sensors in this protection scheme. The scheme performs dynamic state estimation with high-frequency measurements provided by the sensors, assesses the operating condition (i.e., health) of the transmission line in real-time, and thereby determines the tripping signal whenever a fault is detected. The validation was carried out in two steps, first with simulation studies for a three-phase fault and then with the experimental implementation using a physical scaled-down model of a power system consisting of transmission lines, transformers, and loads. The simulation and validation results have shown that the scheme performs adequately in both normal and fault conditions. In the fault case with the experimental setup, the scheme could correctly detect the fault and send the trip signal to the line’s circuit breakers with a total fault clearing time of approximately 65 milliseconds which is comparable to conventional protection methods. The average processing time for a measurement sample block is 12.5 milliseconds. The results demonstrate that this scheme and the sensors would work for transmission line protection which can avoid relay coordination and settings issues.

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers.

This paper presents a cost-based TSO-DSO coordination model to quantify the value of local flexibility services and analyze its impact on the transmission grid expansion and the system operation. Flexibility is provided to the DC power flow transmission grid model by microgrids within the integrated AC power flow distribution grid model. The model's objective is to minimize the overall cost of transmission investments and procured flexibility and is achieved using a bilevel optimization approach where the power exchanges on all connected grid interfaces are controlled. Case studies using a combined test system of the IEEE RTS-96 transmission network interfacing multiple 33-bus distribution grids were performed to validate the model and assess the values and impacts of local flexibility on the transmission system expansion. The results showed that the proposed model modified the investment plan and dispatch of flexibility resources reducing the investment and operation cost of the transmission system.

Protection of microgrid has become challenging due to the hosting of various actors such as distributed generation, energy storage systems, information and communication technologies, etc. The main protection challenges in the microgrid are the bi-directional power flow, protection blinding, sympathetic tripping, change in short-circuit level due to different modes of operation, and limited fault current contribution by converter-interfaced sources. This paper presents a comprehensive review of the available microgrid protection schemes which are based on traditional protection principles and emerging techniques such as machine learning, data-mining, wavelet transform, etc. A categorical assessment of the reviewed protection schemes is also presented. The key findings of the paper suggest that the time-domain and communication-assisted protection schemes could be suitable solutions to address the identified protection challenges in the microgrid.

The increasing volume of distributed resources and user-dependent loads in local networks has increased the concern for congestion and voltage management in distribution networks. To mitigate these issues, the implementation of local flexibility markets has been proposed to assist distribution system operators (DSOs) to manage their networks efficiently. This paper presents the framework of a local flexibility market, including the market participants and their roles. This framework aims to empower DSOs with a market-based instrument for the alleviation of congestion incidents by exploiting the flexibility of local resources. The proposed market aims to provide a tool for the holistic management of distribution networks by trading both reservation and activation of flexibility services, indifferent of the type and the timeline of the needed service. Three market modes are proposed, i.e., long-term, short-term and real-time market, and the interactions among those modes are shown. The operation of the market is explained in detail, including the identification of the needed services, the activation of the market as well as the proposed bidding, clearing and settlement mechanisms. The modelling of the long-term and real-time markets is also presented, along with some indicative simulation results for long-term and real-time services. Finally, the future developments as well as the major conclusions are discussed.

Electric distribution systems in Europe are facing significant challenges due to climate change goals, changing market frameworks and technological innovations, which will significantly impact the role of the distribution system operators. The nature and scale of these challenges are strongly driven by the European vision and strategies on climate and energy. This study identifies which policies can become barriers for distribution grid innovation and the implementation of advanced smart grid solutions developed within the UNITED-GRID project. After an extensive review of new policy priority areas within the energy and climate framework and electricity market design, and subsequent discussions with three partner distribution system operators, five priority barriers are identified. The results show that ambitious decarbonisation targets and changing expectations on the distribution system operators’ role in the energy system would require more flexible and efficient network management. However, binding income frameworks, lacking incentives for innovation and regulatory uncertainties hinder modernisation in distribution systems. It can be concluded that these concerns increase the risks for distribution system operators and have to be considered by research projects and developers of smart grid solutions in order to implement and achieve market uptake of the developed solutions within the next 5–10 years.

Electric buses have gained a large public interest lately. In Gothenburg, Sweden a demonstration project is currently underway where electrical and hybrid electric buses are being tried. This paper investigates the impacts on the electrical distribution system from the fast charging stations used to power the electric buses. Alternatives to mitigate the impacts including demand response and energy storage at the charge location have also been investigated in the paper. A case study has been performed for the distribution system of Chalmers University of Technology where a charging station is located. The study results show that in all of the simulated areas, the grid could handle a 300 kW charge station without any problems. Energy storage and demand response could, to some extent, support the grid to manage the fast charging stations but would be limited by the utilization factor of the charger.

Integration of more distributed renewable energy resources (RES) technologies in the past few years has led to reliability and stability issues in the energy systems due to the mismatch between the demand and the intermittent RES production. Integrated community energy systems (ICES) have been discussed as part of the solution for these challenges. In this paper, we have assessed the impact of the internal energy exchange (IEE) fee in ICESs with a model-based approach. The impact is considered for the performance of ICESs and the behaviour of different prosumers’ assets such as batteries, thermal energy storage, auxiliary heat pumps and demand response. A case study has been carried out on a district in Gothenburg, Sweden and the results show that the ICESs’ performance and the assets’ behaviour are highly affected by IEE fee and the ICES chooses different cost saving strategies at different IEE fees. For the case study, the community’s cost was reduced by 30%, and the electricity import and export by 45.6% and 97.2% respectively when low IEE fee were considered.

This paper proposes a framework for energy management in modern smart buildings under the context of integrated energy systems. The added value of this work is using the rolling time window framework for decision-making, along with the inclusion of the peak load tariffs. The rolling time window is used to send updated control commands near to real-time. A Swedish multi-family residential building is used in the case study. The results of the case study verify the effectiveness and the validity of the proposed model. The results show that by integrating the electricity and the district heating network with a 11 kW heat pump, the total energy cost reduces around 27%.

This paper focuses on the optimal energy management of grid-connected microgrids with battery energy storage systems. The microgrid energy management and the optimal power flow of the distribution network are formulated as mixed-integer linear optimization problems to evaluate microgrid energy scheduling strategies including cost minimization, maximum use of own resources, and minimum energy exchange with the upstream network. The real distribution network of Chalmers University of Technology campus is used as a case study. The study results show that economic optimization yields an annual microgrid cost reduction of 4%. Alternatively, if the microgrid minimizes the energy exchange, virtual islanding operation (zero energy exchange) for 3211 hours can be achieved within a year. The results also present the effects on the operation and cost of the distribution system and highlight a trade-off between microgrid cost minimization and battery lifetime.

This paper proposes a close to real-time energy management system for multi-family residential buildings. The aim of the model is to optimally manage the controllable energy sources in a rolling time window to reduce the total energy cost of the building, considering the time variable electricity prices. A mixed-integer linear programming model is developed to control the washing machines, dishwashers and the energy storage system in a smart building. The performance of the model is evaluated through simulating the operation of an energy management system during one day using the measurement data from a real building located within Chalmers University of Technology campus. The results show that the proposed model can help reduce the building’s energy cost by 9% and reduce the peak demand 18.78% using the resource flexibilities in the building, without causing discomfort for the tenants.

This paper presents an overview of a testbed for intelligent distribution grids, local energy systems, and energy flexible buildings, which is being developed at the campus of Chalmers University of Technology in Gothenburg, Sweden. It describes the test sites, the functionalities, and the planned demonstration activities within the scope of on-going research projects. The proposed demonstrations include a local energy market platform, energy management solutions for microgrids and smart buildings, as well as voltage control in distribution grids. The paper aims to show how the physical energy supply systems of the university are being adapted to integrate the communication and control set-ups that provide the technical requirements for smart grid interoperability. As an example, the on-site implementation of remote battery control is presented, where initial results show the feasibility and potential benefits of the external control. Finally, challenges and lessons learned during the development of the testbed are highlighted.

In this paper, an optimal stochastic operation scheduling model is proposed for a prosumer owning photovoltaic (PV) facility coupled with a Battery Energy Storage System (BESS). The objective of the model is to maximize the prosumer’s expected profits. A two-stage stochastic mixed- integer nonlinear optimization (SMINLP) approach is used to cope with the parameters’ uncertainties. Artificial Neural Networks (ANN) are used to forecast the markets’ prices and the standard scenario reduction algorithms are applied to handle the computational tractability of the problem. The model is applied to a case study using data from the Nordic electricity markets and historical PV production data from the Chalmers University of Technology campus, considering a scaled up 5MWp power capacity. The results show that the proposed approach could increase the revenue for the prosumer by up to 11.6% as compared to the case without any strategy. Furthermore, the sensitivity analysis of BESS’s size on the expected profit shows that increasing BESS size could lead to an increase in the net profits.

Increasing amount of renewable based distributed generation at distribution systems, leads to an increased need for active distribution network management dealing with local network congestion and voltage issues. Development of local flexibility markets aims to provide a market-based solution to these issues. This paper presents a comprehensive review of proposed approaches towards markets exploiting the flexibilities from the demand-side. Efforts have been made on presenting a systematic overview of market design, including e.g. framework, participation, bidding and clearing mechanisms, of local flexibility market proposals developed in recent years. The implementation and regulatory issues and challenges are also discussed. The paper also presents the conceptual framework of the local flexibility service market which is currently being developed within UNITED-GRID project. This proposal aims to provide a holistic approach on local service markets, so that Distribution System Operators (DSOs) are provided with a market-based instrument to manage their networks efficiently.

This paper presents a congestion forecast framework for electrical distribution systems with high penetration of solar photovoltaic and plug-in electric vehicles. The framework is based on probabilistic power flow to account for the uncertainties in photovoltaic production and load demand. The proposed framework has been implemented and tested using the data of the real distribution grid of Chalmers University of Technology campus. Cases studies have been carried out using the framework to analyse the impact of different local production levels and operating modes of solar photovoltaic inverter. The results have shown that cumulative probability for network congestion in branches and transformers would increase by 30% and 20% respectively, when the level of local PV generation, demand and PEVs demand to increase by 100%, 95% and 100% respectively. Also, results have shown that network congestion in branches and transformers is 4% and 8% respectively, more likely to occur in the constant-V mode as compared to constant-pf mode. These results can help distribution system operators to predict any upcoming congestion in their system and subsequently help them in taking suitable actions in order to mitigate congestion.

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

This paper presents a cost-benefit approach for evaluation of battery energy storage (BES) options to be installed in the electrical distribution grid of Chalmers University from the microgrid perspective. The evaluation is based on a multi-period ac optimal power flow model applied for microgrid, which is referred to as the μ-OPF, with the objective function being the total grid operation cost. The model is developed for the real 12 kV grid of Chalmers. The μ-OPF is used as the calculation tool for the cost-benefit analysis where the benefit-to-cost ratios (BCR) of battery options in the grid in a year over the annualized cost of battery are evaluated for various options of battery sizes in the grid. The best location for the battery storage was however determined with the minimum loss criterion. The BCRs demonstrate the cost-effectiveness of the battery in microgrid operation. Battery options considered include both distributed option and a centralized option. It has been found that the best location of battery storage at Chalmers grid is at the point of connection to upstream grid of Gothenburg Energy distribution system for the centralized option. The study results show that the benefit gained from using battery storage increases with the size of battery, and the size with the highest BCR was found to be 2 MWh when considering the grid-connected mode of operation. The optimal size when the grid is in island mode is however dependent on island mode capability.

The electrification of the public transportation system will likely lead to an increased need of reinforcement in the electrical distribution system. This paper presents an investigation on the impacts of fast charging stations for electric buses on the distribution system. The use the fast charging stations for reactive power compensation (RPC) is presented and the possible benefits for the distribution system is evaluated. The results have shown that by utilizing the charger for RPC, the hosting capacity for electric buses of the distribution system could be increased by up to 13% while network losses could be reduced by approximately 1–2%. However, utilizing the charger for RPC would increase the losses within the converter which could be higher than the gain in network losses. Furthermore the possible benefit depends, to a large extent, on the location of the charge station and the strength of the distribution system.

A massive introduction of plug-in electric vehicles (PEVs) in the transportation sector would likely increase the total electricity consumption. Depending on how and when the PEVs are charged, the effects on the power demand will vary. Previous studies have shown that uncontrolled charge of PEVs can cause problems for the distribution system in some areas. This paper proposes an approach to control PEVs charging based on the charging behavior estimated from the demographical statistical data. In this approach, three different charge strategies are designed and the impacts of PEVs charging on the distribution system is assess using standard load flow calculations. A case study using the proposed approach examines the real situation in Gothenburg, a city on the west coast of Sweden. The results from the study show that the impacts on the distribution system due to PEVs vary between different areas. By controlling the charging, the impacts can be reduced but the choice of control methods must be chosen carefully. Furthermore, the results indicate that a well-developed public charging infrastructure could reduce the stress on the residential distribution systems since part of the charging can be done in commercial areas.

This paper provides an approach to investigate the impacts of wind power (WP) and demand side management (DSM) on the day-ahead electricity market prices. A market model incorporating demand scheduling has been developed for the study, to assess whether there are any mutual economic benefits between WP and scheduling of electric heating demand. A case study performed on a representative Nordic system with a scenario for future WP development in the Nordic electricity market has been carried out. The study results show that DSM could reduce the revenue for the WP producers, up to 2%. On the other hand, WP could result in reduced heating cost, up to 10%, for the customers. However, with an objective of reducing the total system cost, customers in some areas could experience an increase in electricity price even after providing their flexible demand to the market.

Integration of Electric vehicles and their impact on power system have been a major topic within smart grid initiatives. It is proven that managing larger amount of loads in form of plug-in Electric vehicles (PEV) is a challenge for operation of low and medium voltage distribution network. The main objective of this paper is to discuss the usage of “Integrated Volt/Var Optimization” function (which is one of main advanced distribution management system (DMS) functions) for handling the larger share of PEV load and reducing their impact on operation of Distribution System (DS). For this purpose, we propose a method for evaluating the impact of PEV charging on steady state operating condition of DS and identifying its possible capacity limitations in case of significant penetration of PEVs. We have applied a stochastic modeling for base EV load and have examined several different scenarios based on charging power and penetration level of PEVs to compare uncontrolled charging with base operation conditions. By presenting the results from our developed Volt/Var Optimization (VVO) engine, it is concluded that DMS functions can support handling of these new operating conditions for DSOs. A real distribution network in south western part of Sweden is used as test system for this study while a set of realistic load profile has been created based on real driving pattern (using the results of national survey from Swedish traffic authority) and actual base load for one year. In this work, the VVO function have been implemented in General Algebraic Modeling System (GAMS) by using a single mixed integer linear programming (MILP) model for the volt-var problem. The results of optimization are system loss and voltage profile along the network in comparison with “base-case” solution.

Customers with hourly electricity pricing can reduce their electricity cost by shifting their electricity demand in time. However, it is not known how much different customer could gain. As most hourly pricing contracts in Sweden are designed today, customers will know the electricity prices 12-36 hours in advance and will have the possibility to schedule their electricity usage in order to reduce their cost of electricity. In this study, three houses with different load profiles and heat demand are investigated to evaluate the possible savings that could be achieved by adapting to hourly pricing. Heating, laundry machines and dishwashers are assumed to be flexible loads that could be shifted in time. The study results show that the economic incentives to reschedule loads based on the hourly pricing will be limited for customers without electric heating. Even for customers with electric heating, the economic incentives may be too low to adapt to hourly tariffs based on the day-ahead spot market.

The electric power system is changing and, especially, the electrical distribution system will meet new features. One of the large changes is a transformation of the transportation sector with the use of electricity, with introduction of Plug-in Electric Vehicles (PEVs). This will pose new challenges and opportunities for the electric distribution companies. This paper analyzes the effects of PEVs charging on the local 400 V and 10 kV electric distribution systems in the city of Gothenburg, Sweden, using steady-state power flow analysis. Two different areas representing residential area and commercial area have been simulated for the worst-case scenario, which is simultaneous charging of all the vehicles during the peak load period. The number of PEVs used has been estimated based on the load level at each customer location. The study results have shown that overloading of lines and transformers would occur when simultaneous charging of the vehicles during the peak load period. There would, however, be no problem with the voltage drop at the customer's location during PEVs charging. An iterative method is proposed to estimate the maximum number of possible vehicles charging in the distribution system without resulting in any congestion. This method is also applied when one of the feeders is on outage to ensure the N-1 reliability criterion.

Integration of plug in hybrid electric vehicles and electric vehicles (PHEVs and EVs) includes a wide area of topics like grid effects, different charging concepts, charger designs, harmonics from the charger etc. This short paper gives an introduction to ongoing work within this field in Sweden, and shows on research work within the topic at Chalmers University of Technology.

HepaRG cells, derived from a female hepatocarcinoma patient, are capable of differentiating into biliary epithelial cells and hepatocytes. More importantly, differentiated HepaRG cells are able to maintain activities of many xenobiotic-metabolizing enzymes, and expression of the metabolizing enzyme genes can be induced by xenobiotics. The ability of these cells to express and induce xenobiotic-metabolizing enzymes is in stark contrast to the frequently used HepG2 cells. The previous studies have mainly focused on a set of selected genes; therefore, it is of significant interest to know the extent of similarity of gene expression at whole genome levels in HepaRG cells and HepG2 cells compared with primary human hepatocytes and human liver tissues. To accomplish this objective, we used Affymetrix (Santa Clara, CA) U133 Plus 2.0 arrays to characterize the whole genome gene expression profiles in triplicate biological samples from HepG2 cells, HepaRG cells (undifferentiated and differentiated cells), freshly isolated primary human hepatocytes, and frozen liver tissues. After using similarity matrix, principal components, and hierarchical clustering methods, we found that HepaRG cells globally transcribe genes at levels more similar to human primary hepatocytes and human liver tissues than HepG2 cells. In particular, many genes encoding drug-processing proteins are transcribed at a more similar level in HepaRG cells than in HepG2 cells compared with primary human hepatocytes and liver samples. The transcriptomic similarity of HepaRG with primary human hepatocytes is encouraging for use of HepaRG cells in the study of xenobiotic metabolism, hepatotoxicology, and hepatocyte differentiation.

The main purpose of this paper is to investigate how network tariffs, such as the traditional energy-based network tariff (EBT) and power-based network tariffs (PBT), would affect the distribution system and the customers’ incentives to schedule their demand under an hourly electricity pricing scheme. For this purpose, a mixed integer linear programming model has been developed and used in a case study to schedule the load demand for 100 residential customers with the objective being minimization of their electricity cost. The results have shown that by scheduling the flexible loads, customers could save up to €119/year under EBT and €127/year under PBT. If more than 25% of the customers were price-responsive under the EBT, the peak demand could be increased, while it could be reduced by 4% if all customers were responsive under the PBT. With plug-in electric vehicles, the possible benefits were found to be higher for both customers and the distribution system operator for the case with PBT compared with EBT.