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This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

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In demand response programs, the load aggregator would send requests to customers to confirm whether they are willing to participate in the demand response events later, making sure that there is sufficient capacity to meet the load adjustment requirement of the power grid in real-time. However, sending requests frequently to the customers would lead to customer fatigue effect, which dampens customers’ enthusiasm for demand response. Motivated by this dilemma, an optimization problem of scheduling customers considering fatigue based on the MAB framework is proposed, and the online learning and ranking method: fatigue-aware MAB is presented to solve it. Finally, the numerical simulation demonstrates that the proposed method outperforms the traditional method under different parameter settings. Detailed analysis of how those settings affect the performance of our proposed method is also provided.

Energy Internet technology can realize the information fusion of demand side adjustable power load resources, transmission and distribution resources, power generation resources, and play the value benefits of demand side adjustable power load in peak load and frequency regulationof power grid. In this paper, by integrating the advantages and disadvantages of different control modes for demand side resources, a framework and method of decentralized collaborative control within demand side resource cluster and regional grid coordinated control is proposed, which can effectively improve the flexibility and rapidity of adjustable resource response grid frequency regulation.

Power to Heat (P2H) technology is seen as an effective solution for integrating stochastic wind power. Particularly in real-time electricity markets, high-precision wind power forecasts enable the load to better accommodate its fluctuations. Nonetheless, the shorter time scales of real-time trading pose challenges for efficiently aggregating a large, heterogeneous, and dispersed loads and fully harnessing the thermal storage capabilities of loads to follow wind power fluctuations across multiple periods. Against this backdrop, this paper proposes an aggregation control approach for P2H loads with considering multi-period Stackelberg game strategy within the framework of a load-oriented virtual power plant (VPP). For multi-period real-time trading, a Stackelberg game model based on a dynamic price ceiling is established with considering dynamic supply-demand balance between wind power and P2H loads; To enhance load aggregation effectiveness, a dynamically updated temperature-power aggregation model is developed by employing model order reduction, variables continuousization, and linear superposition on second-order equivalent thermodynamic model; To improve load decomposition efficiency, a hierarchical and multi-step control strategy encompassing the continuousization of discrete variables is utilized to simplify the problem. Finally, a simulation example is presented using actual data from a power grid in northern China to validate the effectiveness of the proposed method. • Multi-period Stackelberg game model considering dynamic price ceilings. • Dynamic aggregation for P2H loads based on power-temperature feasible domain. • Load decomposition optimization using the continuous model of P2H load. • Enhancing accommodating wind power while ensuring long-term users benefit. • Improved optimization efficiency and speed in load aggregation and decomposition. [ABSTRACT FROM AUTHOR]

With electrical performance similar to that of energy storage systems, electric vehicles (EVs) show great potential for demand response to facilitate higher penetration of distributed renewable energy. Therefore, this article presents a look-ahead scheduling model for EVs to offer a load curtailment service in a distribution network, with the aim of smoothing out the power fluctuations caused by distributed renewable energy sources. An equivalent aggregation method that is not sensitive to heterogeneous private parameters is used to extract the overall dynamic power regulation characteristics of the EV population, while a decomposition model ensures the completion of the regulation task. Besides, in the scheduling model, the variable baselines are dynamically refreshed to capture the load rebound effects caused by load curtailment, allowing more accurate regulation plans to be obtained. A two-stage iterative solution strategy is also adopted to obtain the approximate optimal solution. Simulations demonstrate both the accuracy of the aggregation model and the effectiveness of the scheduling model. [ABSTRACT FROM AUTHOR]

Low frequency electromechanical oscillations can pose a threat to the stability of power systems if not properly addressed. This paper proposes a novel methodology to damp these inter-area oscillations using loads, the demand side of the system. In the proposed methodology, loads are assigned to an aggregated cluster whose demand is modulated for oscillation damping. The load cluster control action is obtained from an optimal output feedback control (OOFC) strategy. The paper presents an extension to the regular OOFC formulation by imposing a constraint on the sum of the rows in the optimal gain matrix. This constraint is useful when the feedback signals are generator speeds. In this case, the sum of the rows of the optimal gain matrix is the droop gain of each load actuator. Time-domain simulations of a large-scale power system are used to demonstrate the efficacy of the proposed control algorithm. Two different cases are considered: a power imbalance and a line fault. The simulation results show that the proposed controllers successfully damp inter-area oscillations under different operating conditions and with different clustering for the events considered. In addition, the simulations illustrate the benefit of the proposed extension to the OOFC that enable load to provide a combination of droop control and small signal stability augmentation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to mitigate the problem of high power demand peak and load oscillations in the operation of a large population of thermostatically controlled loads (TCLs) operated by model predictive control (MPC) at the TCL level. Two desynchronized MPC schemes are introduced: 1) adding random delays in reference signals and 2) extra penalizations on MPC objective functions. For characterizing and validating the proposed desynchronization MPC schemes, a partial differential equation (PDE) model is developed to represent the evolution of the operational states of the TCLs controlled by MPC in a population. The focus of this paper is put on the control of cooling fans in server racks of datacenters, whereas the proposed approach is applicable to other types of TCLs. Numerical simulation studies are carried out and the obtained results confirm the validity and the applicability of the developed approach. [ABSTRACT FROM AUTHOR]

This paper reports the recent work carried out to engage a pool of large industrial end-users in frequency control complying with the performance criteria of conventional generation. The pooling approach unites the benefits of continuous loads and staggered loads (discrete active power response). Based on a local measurement of the frequency, the primary control signal is dynamically reconstructed through a centrally managed automation infrastructure integrating the industrial constraints and the requirements of the transmission system operators (TSO). The regulation algorithm, minimizing the total number of up and down activations of the loads, as well as the regulation infrastructure, are presented and detailed. After a discussion of the practical implementation at three distant industrial sites, the response of the experimental installation of the pool is discussed through its performance measurements and assessment. From the industrial point of view, the overall regulation system is in operation, adding value to the power system and the end-users and is reported to be economically viable. The implemented entity has been certified by the French TSO RTE. [ABSTRACT FROM AUTHOR]

• Aggregation of loads across apartment buildings increases self-consumption of PV generation. • Load aggregation through embedded network can give financial benefits to customers. • Shared PV generation can add customer value to embedded network. • PV distributed behind-the-meter avoids regulatory barriers of embedded network. As subsidised feed-in-tariffs for distributed photovoltaic (PV) generation are reduced or abolished in many jurisdictions, there is growing interest in increasing self-consumption to realise greater value from rooftop PV generation. However, deployment of PV on apartment buildings lags behind other residential deployment despite the potential advantages of load aggregation. We present a study of electricity and financial flows in ten 'virtual' Australian apartment buildings under a range of technical implementations and financial arrangements, using real load profiles and simulated generation profiles. Aggregation of diverse household and shared loads, either through an embedded network or 'behind the meter' of individual dwellings, can increase self-sufficiency and self-consumption of on-site generation compared to separate systems supplying common property or individual apartments. While embedded networks can enable access to more beneficial retail arrangements, behind the meter solutions may allow residents to avoid regulatory complexities and additional costs. The relative benefits of each arrangement are dependent on building characteristics and financial settings. [ABSTRACT FROM AUTHOR]

Electricity demand aggregation and demand forecast are prerequisites for establishing any electrical power project, especially for an off-grid power system. Some rural places in Ghana require the use of renewable resources for power generation, thus, must be carefully planned to avoid oversized or undersized systems. This research was carried out to aggregate the power demand of Buoya community in Ghana and a five-year forecast modeled in MATLAB using an inverse matrix method was made. Load aggregated through survey and appliance enduse method was used to calculate the demand for the community for a possible future bio-power electrical system. Different power supply scenarios were modeled for possible adoption to ensure a cost-effective supply of bio-power to the community. In the first year of implementation, a daily capacity of 45.37 kW was realised with an energy demand of 373.55 kWh. This is expected to grow to 72.1 kW with the energy demand of 718.6 kWh in five years. This growth is estimated at 70% over the period due to the lifestyle changes of the people. In the interim, as the community's productive use of power is low coupled with lower demand factor, this research proposes a plan of periodic daily power supply to cut down on fuel resources and cost of power. A scenario of 3-hour morning supply, a 2-hour mid-day supply, and a 4-hour evening supply plan is recommended for the first year of power system implementation before a full daily supply plan can be rolled out later. This finding is seen as applicable to most rural places that lack grid power supply and yet abound in renewable energy resources. [ABSTRACT FROM AUTHOR]

Understanding of residential electricity demand has application in efficient building design, network planning and broader policy and regulation, as well as in planning the deployment of energy efficiency technologies and distributed energy resources with potential emissions reduction benefits and societal and household cost savings. Very few studies have explored the specific demand characteristics of apartments, which house a growing proportion of the global urban population. We present a study of apartment electricity loads, using a dataset containing a year of half-hourly electricity data for 6,000 Australian households, to examine the relationship between dwelling type, demographic characteristics and load profile. The focus on apartments, combined with the size of the data set, and the representative seasonal load profiles obtained through clustering full annual profiles, is unique in the literature. We find that median per-occupant household electricity use is 21% lower for apartments than for houses and that, on average, apartments have lower load factor and higher daily load variability, and show greater diversity in their daily peak times, resulting in a lower coincidence factor for aggregations of apartment loads. Using cluster analysis and classification, we also show the impact of dwelling type on the shape of household electricity load profiles. Image 109771 • Median per-occupant energy use 21% lower for apartments than for houses. • Apartment loads have higher variability, lower load factor and coincidence factor. • After diversity aggregation benefits significant for up to fifty residential loads. • Dwelling type is a significant factor in clustering of household load profiles. • The high diversity of household load profiles precludes simple categorisation. [ABSTRACT FROM AUTHOR]

As the progress of electrification for the public transportation sector is accelerated, it becomes more and more important to integrated planning charging infrastructure for public transportation, while emerging technologies such as distributed renewable generation and energy storage system should be accounted. Here, an integrated resource planning framework is proposed where both planning investment cost and operational cost are considered. An aggregation strategy is also proposed to optimise the charging decisions for electric bus on different routes, which could effectively improve the planning and operation efficiency. To address the uncertainties involved in this process, a scenario‐based chance‐constrained programming approach is adopted. A numerical case study is also included to demonstrate the effectiveness of this proposed resource planning model. [ABSTRACT FROM AUTHOR]

The increasing penetration of PV into the distribution grid leads to congestion, causing detrimental power quality issues. Moreover, the multiple small photovoltaic (PV) systems and battery energy storage systems (BESSs) result in increasing conversion losses. A low-voltage DC (LVDC) backbone to interconnect these assets would decrease the conversion losses and is a promising solution for a more optimal integration of PV systems. The multiple small PV systems can be replaced by shared assets with large common PV installations and a large BESS. Sharing renewable energy and aggregation are activities that are stimulated by the European Commission and lead to a substantial benefit in terms of self-consumption index (SCI) and self-sufficiency index (SSI). In this study, the benefit of an LVDC backbone is investigated compared to using a low-voltage AC (LVAC) system. It is found that the cable losses increase by 0.9 percent points and the conversion losses decrease by 12 percent points compared to the traditional low-voltage AC (LVAC) system. The SCI increases by 2 percent points and the SSI increases by 6 percent points compared to using an LVAC system with shared meter. It is shown that an LVDC backbone is only beneficial with a PV penetration level of 65% and that the BESS can be reduced by 22% for the same SSI.

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.

Highlights • Multiple thermostatically controlled load groups are coordinated to deal with the power fluctuation. • A proportion load aggregation and an optimal task decomposition are developed. • A hierarchical and iterative algorithm is proposed to solve the two-stage optimization model. • Both net exchange power fluctuation and load regulation costs are all reduced in standard system. Abstract As the increasing penetration of distributed renewable energy sources, the inherent fluctuation of their output power aggravates more control burden for the power system. Based on the direct load control, the regulation potential of distributed flexible loads can be developed to relieve this pressure. This paper proposes a real-time two-stage optimization model for multiple thermostatically controlled load groups to smooth the power fluctuation in distribution network. The upper-level evaluates the load regulation capacity and suppresses the net exchange power fluctuation; the lower-level further minimizes the regulation costs and makes the target demand curves for each load group, which can guide the subsequent load power tracking. Besides, within the load group, a proportion aggregation method is applied to reflect its external power regulation characteristics, while the decomposition program assigns the regulation task to each terminal user. For the complex problem established above, a hierarchical and iterative solving strategy is proposed to obtain the approximate optimal solution. The simulation results show that the proposed approach can effectively decrease the net exchange power fluctuation as well as regulation costs. [ABSTRACT FROM AUTHOR]

In the traditional power system, most of the frequency response services are provided from expensive large synchronous generators. However, demand is an effective element to provide frequency response with less expenditure. This paper discusses the availability of domestic heat pumps and fridges load to provide frequency response service to the Great Britain power system. The data of the low and high-frequency response from heat pumps is supplied by Element Energy while Open Energi provides data of the availability of fridges over the time of day. The potential of frequency response from the aggregation of heat pumps and fridges without undermining the inherent function of the load was estimated at a time of day. The combination of both loads to provide Firm Frequency Response (FFR) service was also investigated. Frequency control was developed to alter the power consumption of the aggregated load in response to a frequency signal. Simulation results showed that the aggregation of the load is an effective way to provide a dynamic frequency response over a day. [ABSTRACT FROM AUTHOR]

In a transactive energy market, distributed energy resources (DERs) such as dispatchable distributed generators (DGs), electrical energy storages (EESs), distribution-scale load aggregators (LAs), and renewable energy sources (RESs) have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO) may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP), is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP) formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP), which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Aggregations of flexible loads can provide several power system services through demand response programs, for example load shifting and curtailment. The capabilities of demand response should therefore be represented in system operators’ planning and operational routines. However, incorporating models of every load in an aggregation into these routines could compromise their tractability by adding exorbitant numbers of new variables and constraints. In this paper, we propose a novel approximation for concisely representing the capabilities of a heterogeneous aggregation of flexible loads. We assume that each load is mathematically described by a convex polytope, i.e., a set of linear constraints, a class which includes deferrable loads, thermostatically controlled loads, and generic energy storage. The set-wise sum of the loads is the Minkowski sum, which is in general computationally intractable. Our representation is an outer approximation of the Minkowski sum. The new approximation is easily computable and only uses one variable per time period corresponding to the aggregation’s net power usage. Theoretical and numerical results indicate that the approximation is accurate for broad classes of loads. [ABSTRACT FROM AUTHOR]

Integrating renewable energy resources (RES) is a challenge for power system operators due to their fluctuations and unpredictability. At the same time, the water shortage problem and the needs to desalinate more freshwater increase the prominence of sufficient energy resources for sustainable operation. Therefore, this paper presents a market-clearing mechanism in a co-optimization model that coordinates the operation of grid-connected reverse osmosis water desalination plants (RO-WDPs) and the operation of renewable-rich power systems. It is assumed that electric demands can participate in the provision of demand response (DR) to the market via multiple DR options. The DR options related to the general loads are demand shifting, load curtailment, distributed generation, and hybrid energy storage systems (HESS). These HESS includes battery storage and hydrogen storage systems. Pertaining to their special characteristics, a new DR option is proposed as a customized option for RO-WDPs. The market clearing mechanism is assumed to be based on the security-constrained unit commitment (SCUC) and formulated as a stochastic mixed-integer linear programming problem (MILP) to optimally schedule the day-ahead operation of the power system. The presented model is applied on 6-bus and IEEE 24-bus reliability test system (RTS) test power systems with significant penetration of RES to demonstrate its merits. The simulation results show that the system efficiency is enhanced by adding the energy flexibility of RO-WDP without endangering the water demand-supply when using the proposed coordinated model. Hence, the total operation cost is minimized, the RES integration is facilitated, and the hourly electricity prices are smoothened. • We propose coordination model of grid-connected RO desalination and responsive power system. • The power system in our model considers five DR options including hybrid ESS. • We avoid the generic DR model to reschedule the RO desalination flexibility. • The day-ahead operation of our energy–water nexus incorporates system uncertainties. • The incorporated approach minimizes cost and CO2 and enhances the system flexibility. [ABSTRACT FROM AUTHOR]

Demand response (DR) is an important component for the establishment of smart electricity grids. It can decrease the system peaks through load shedding or shifting and optimize the utilization of the existing grid assets, which delays the need for costly upgrades. DR can also enable the integration of intermittent and distributed energy resources (DER) into the existing electricity grid. Fast DR from aggregated flexible loads can provide ancillary services (AS) to absorb grid disruptions and may replace the expensive fast-ramping reserve generation units. This study presents a methodology for load aggregation based on the prioritization of loads according to their flexibility. Different flexible load types are categorized as thermostatically controlled loads (TCL), urgent non-TCL, non-urgent non-TCL, and battery-based loads. Models based on their physical behaviour are developed and simulations performed to apply the proposed aggregation and control algorithm. Results show that the loads during peak hours can be shed off without rebound demand spikes after the DR event commonly seen in other types of DR programs. The algorithm also automatically adjusts the power demand according to the output of the distributed renewable generation, mitigating disruptions due to variations of the DER output. Additionally, the algorithm is able to adjust the load demand dynamically according to the fluctuations of electricity price. [ABSTRACT FROM AUTHOR]

In a transactive energy market, distributed energy resources (DERs) such as dispatchable distributed generators (DGs), electrical energy storages (EESs), distribution-scale load aggregators (LAs), and renewable energy sources (RESs) have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO) may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP), is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP) formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP), which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model.

Load control (LC) of populations of air conditioners (ACs) is considered suitable to shift energy from on- to off-peak times, and track the intermittent power output of renewable generation. From a technical and economical point of view, it is paramount to quantify the amount of energy that can be saved by implementing these LC events. This paper proposes a new causal methodology to estimate such energy savings using a Kalman filter that includes a parametric second-order model of the aggregate demand of a population of ACs. The proposed methodology relies only on readings of aggregate electrical power at the feeder level and does not require historical load data, or a control group, and hence, it can be used where other methods reported in the literature are inapplicable. The proposed estimator is evaluated on a numerical case study that embeds simulated ACs in real power and temperature data from a 70-house residential precinct. [ABSTRACT FROM PUBLISHER]

In this paper, the coordinated integration of aggregated plug-in electric vehicle (PEV) fleets and renewable energy sources (wind energy) in power systems is studied by stochastic security-constrained unit commitment (Stochastic SCUC) model, which minimizes the expected grid operation cost while considering the random behavior of the many PEVs. PEVs are mobile and distributed devices with deferrable options for the supply/utilization of energy at various times and locations. The increased utilization of PEVs, which consume electricity rather than fossil fuel for driving, offers unique economic and environmental opportunities, and brings out new challenges to electric power system operation and planning. The storage capability of PEVs could help power systems mitigate the variability of renewable energy sources and reduce grid operation costs. Vehicle-to-grid (V2G) enables PEVs to have bi-directional power flows once they are connected to the grid, i.e., they can either inject power to, and draw power from, the grid which adds further complexity to power system operations. PEVs signify customers' random behavior when considering their driving patterns, locational energy requirements, topological grid interconnections, and other constraints imposed by the consumers. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of PEVs on the grid operation cost and hourly wind energy dispatch. [ABSTRACT FROM PUBLISHER]

Large groups of electrical loads can be controlled as a single entity to reduce their aggregate power demand in the electricity network. This approach, known as load management (LM) or demand response, offers an alternative to the traditional paradigm in the electricity market, where matching supply and demand is achieved solely by regulating how much generation is dispatched. Thermostatically controlled loads (TCLs), such as air conditioners (ACs) and fridges, are particularly suitable for LM, which can be implemented using feedback control techniques to regulate their aggregate power. To achieve high performance, such feedback control techniques require an accurate mathematical model of the TCL aggregate dynamics. Although such models have been developed, they appear too complex to be effectively used in control design. In this paper we develop a mathematical model aimed at the design of a model-based feedback control strategy. The proposed model analytically characterises the aggregate power response of a population of ACs to a simultaneous step change in temperature set points. Based on this model, we then derive, and completely parametrise in terms of the ACs ensemble properties, a reduced-order mathematical model to design an internal-model controller that regulates aggregate power by broadcasting temperature set-point offset changes. The proposed controller achieves high LM performance provided the ACs are equipped with high resolution thermostats. With coarser resolution thermostats, which are typical in present commercial and residential ACs, performance deteriorates significantly. This limitation is overcome by subdividing the population into clusters of ACs that receive a coarse-grained, cluster-dependent control signal. The proposed clustering technique recovers the performance achieved with high resolution thermostats at the expense of some additional comfort penalty, which can be quantified using the controller output. [Copyright &y& Elsevier]

Flexible models for aggregated residential loads are needed to analyze the impact of demand response policies and programs on the minimum comfort setting required by end-users. This impact has to be directly deduced from the probability profiles of thermal and electrical performance variables. [ABSTRACT FROM AUTHOR]

[Display omitted] With the recently increasing adoption of transactive energy markets, the role of a load aggregator is becoming increasingly important. Among the many tasks of an aggregator, the task of managing the electricity consumption among a group of grid-interactive efficient buildings according to the demand on the grid is taken up. Specifically, the task of allocating a certain amount of power among the participating customers while respecting their preferences and comfort is considered. Modeling this as an aggregate flexibility, the virtual battery conditions that are employed to verify the feasibility of a candidate power profile are derived. A feasible power profile, when allocated, satisfies specified comfort bands. To determine the ideal power requirement of the loads, the model-free control method is employed. The feasible power profile is allocated by embedding the customers' preferences through weights, posing the power constraint as a hyperplane, and employing a weighted projection from the ideal power requirements onto the constraint plane to solve the considered task. The proposed method is computationally efficient and scalable to any number of heterogeneous thermostatically controlled loads in buildings. [ABSTRACT FROM AUTHOR]

The increasing penetration of PV into the distribution grid leads to congestion, causing detrimental power quality issues. Moreover, the multiple small photovoltaic (PV) systems and battery energy storage systems (BESSs) result in increasing conversion losses. A low-voltage DC (LVDC) backbone to interconnect these assets would decrease the conversion losses and is a promising solution for a more optimal integration of PV systems. The multiple small PV systems can be replaced by shared assets with large common PV installations and a large BESS. Sharing renewable energy and aggregation are activities that are stimulated by the European Commission and lead to a substantial benefit in terms of self-consumption index (SCI) and self-sufficiency index (SSI). In this study, the benefit of an LVDC backbone is investigated compared to using a low-voltage AC (LVAC) system. It is found that the cable losses increase by 0.9 percent points and the conversion losses decrease by 12 percent points compared to the traditional low-voltage AC (LVAC) system. The SCI increases by 2 percent points and the SSI increases by 6 percent points compared to using an LVAC system with shared meter. It is shown that an LVDC backbone is only beneficial with a PV penetration level of 65% and that the BESS can be reduced by 22% for the same SSI. [ABSTRACT FROM AUTHOR]

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers. [ABSTRACT FROM AUTHOR]

• The model is applicable to short- and long-term simulation of geothermal bore fields. • The load aggregation algorithm can use variable time steps during the simulation. • Boreholes can be arbitrarily positioned within the bore field. • The model is validated with analytical, experimental and field monitored data. This paper presents the development and validation of a full-time-scale semi-analytical bore field simulation model. The model allows for the simulation of bore fields comprised of arbitrarily positioned boreholes while accounting for both short-term transient thermal effects within the boreholes and long-term thermal interactions in the bore field. The g -function of the bore field, obtained from the finite line source solution, is corrected to account for the cylindrical geometry of the boreholes and coupled to a thermal resistances and capacitances model of the borehole interior, thereby extending the scope of g -functions to short time scales. Additionally, an improved load aggregation scheme for ground thermal response calculations allows the model to be used with variable simulation time steps. The complete model is validated using a combination of analytical, experimental and field monitored data to verify both its short-term and long-term behaviour. The model is implemented using the Modelica language as part of an implementation in the open-source buildings simulation library IBPSA. [ABSTRACT FROM AUTHOR]

Modern buildings are expected to be not only energy efficient but also energy flexible to facilitate reliable integration of intermittent renewable energy sources into smart grids. Estimating the aggregate energy-flexibility potential at a cluster level plays a key role in assessing financial benefits and service area for energy-flexibility services at design stage and determining real-time pricings at operating stage. However, most existing studies focused on the energy flexibility of individual buildings rather than building clusters. In addition, due to the intrinsic uncertainty in building envelope parameters, performance of building energy systems, and occupancy and occupant behavior, it is necessary to quantify the uncertainty in aggregate energy flexibility. In this study, we developed an approach to quantifying the uncertainty in the aggregate energy flexibility of residential building clusters using a data-driven stochastic occupancy model that can capture the stochasticity of occupancy patterns. A questionnaire survey was carried out to collect occupancy time-series data in Hong Kong for occupancy model identification. Aggregation analysis was conducted considering various building archetypes and occupancy patterns. The uncertainty in aggregate energy flexibility was then quantified based on the proposed performance indices using Monte Carlo technique. With the scaling up of building clusters, the estimated energy-flexibility potential became steady and the weekly energy flexibility stayed around 12.40%. However, the weekly uncertainty of aggregated energy flexibility exponentially decreased from 19.12% for 8 households to 0.74% for 5120 households, which means that the estimate of a building cluster's energy flexibility is more reliable than that of a single building. • A stochastic occupancy model was developed using questionnaire survey data. • Aggregate energy flexibility of residential building clusters was investigated. • Performance indices were proposed to quantify uncertainty in energy flexibility. • Uncertainty in aggregate energy flexibility of building clusters was quantified. [ABSTRACT FROM AUTHOR]