Residential End-users Research Articles

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1&#x2013;3 while Scenarios 4&#x2013;6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>-0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.

Investigating Hydrogen-Based Non-Conventional Storage for PV Power in Eco-Energetic Optimization of a Multi-Energy System

Through the integration of multiple energy carriers with related technologies, multi-energy systems (MES) can exploit the synergies coming from their interplay for several benefits towards decarbonization. In such a context, inclusion of Power-to-X technologies in periods of excess renewable electricity supply, removes the need for curtailment of renewable electricity generation. In order to achieve the environmental benefits of MES without neglecting their economic feasibility, the optimal design problem is as crucial as challenging and requires the adoption of a multi-objective approach. This paper extends the results of a previous work, by investigating hydrogen-based non-conventional storage for PV power in the eco-energetic optimization of an MES. The system under study consists of a reversible fuel cell (r-SOC), photovoltaic (PV), electric heat pump, absorption chiller and thermal storage, and allows satisfying the multi-energy needs of a residential end-user. A multi-objective linear problem is established to find the optimal MES configuration including the sizes of the involved technologies with the goal of reducing the total annual cost and the fossil primary energy input. Simulation results are compared with those obtained in previous work with a conventional nanogrid where a combined heat and power (CHP) system with gas-fired internal combustion engine and a battery were present instead of an r-SOC. The optimized configuration of the non-conventional nanogrid allows achieving a maximum primary energy reduction amounting to 66.3%, compared to the conventional nanogrid. In the face of the environmental benefits, the non-conventional nanogrid leads to an increase in total annual costs, which, compared to the conventional nanogrid, is in the range of 41–65%.

Thermodynamic investigations on 227 kWp industrial rooftop power plant

With the growing demand of energy along with scarcity of natural resources and drastic fluctuations of the climate change implications, there has been a constant effort of mankind to switch towards renewable energy sources. Among various renewable energy systems, solar photovoltaics (SPV) has emerged out as an evident choice for the range of applications from commercial to residential end users. The performance of the SPV power system needs to be monitored, so that the plant can be operated efficiently and maximum electrical output can be generated out of it. For performance assessment, capacity utilisation factor (CUF) has been considered the parameter for monitoring of the SPV power plant. CUF is monitored for the industrial roof top SPV power plant and compared with the other CUF data available in the literature. In the present work, an effort has been made to monitor the CUF parameters, performance ratio (PR) and energy generation units for a 227kWp SPV industrial rooftop power plant. It has been found that due to certain losses, the CUF is found to be lower than the ability of the system. The various technical causes of low CUF along with their remedial actions are proposed in view of improving CUF and overall efficiency of the system.

Microgrid Operation and Control: Challenges and expected functionalities

This article considers several functionalities expected from the emerging microgrids and systems of microgrids. These performance objectives are then related to several modeling- and controlrelated challenges and open R&D questions that must be studied. The challenges are illustrated on Sheriff and Banshee microgrids, which are IEEE standards for testing microgrid control. I suggest that an extended ac optimal power flow (AC OPF) can be used to increase ranges of intermittent resources and responsive demand that can be used in a reliable way and without requiring excessive load shedding. The emphasis is on optimizing voltage-controlled equipment available at the inverter-controlled diverse distributed energy resources (DERs), including electric vehicle (EV) control of small residential end users, and the grid itself. The second question concerns enabling stability of microgrids over broad ranges of system inputs, and I briefly present a novel multilayered interactive modeling in transformed energy state space for stable control design. The article closes by revealing the importance of operating and control paradigm change from today's hierarchical control to interactive dynamic multilayered protocols, which also would provide financial incentives for reliance on control.

Policy options for enhancing economic profitability of residential solar photovoltaic with battery energy storage

Share of solar photovoltaic (PV) is rapidly growing worldwide as technology costs decline and national energy policies promote distributed renewable energy systems. Solar PV can be paired with energy storage systems to increase the self-consumption of PV onsite, and possibly provide grid-level services, such as peak shaving and load levelling. However, the investment on energy storage may not return under current market conditions. We propose three types of policies to incentivise residential electricity consumers to pair solar PV with battery energy storage, namely, a PV self-consumption feed-in tariff bonus; “energy storage policies” for rewarding discharge of electricity from home batteries at times the grid needs most; and dynamic retail pricing mechanisms for enhancing the arbitrage value of residential electricity storage. We soft-link a consumer cost optimization model with a national power system model to analyse the impact of the proposed policies on the economic viability of PV-storage for residential end-users in the UK. The results show that replacing PV generation incentives with a corresponding PV self-consumption bonus offers return on investment in a home battery, equal to a 70% capital subsidy for the battery, but with one-third of regulatory costs. The proposed energy storage policies offer positive return on investment of 40% when pairing a battery with solar PV, without the need for central coordination of decentralized energy storage nor providing ancillary services by electricity storage in buildings. We find that the choice of optimal storage size and dynamic electricity tariffs are key to maximize the profitability of PV-battery energy storage systems.

Auctions in Renewable Energy and the Grid Parity Using Solar Photovoltaic Technology in Peru

This paper, of the network parity situation of photovoltaic solar energy with conventional electrical energy sources, such as hydroelectric and thermoelectric for Peru is analyzed. For this purpose, a review of how the electrical tariff is set for the residential end-user of conventional energy sources has been made. Furthermore, it is shown what it consists about, the cost leveled of energy for the case of photovoltaic solar energy and how this has been used for the calculation of the electric rate of this type of source. A review of the auctions of photovoltaic solar plants that are installed in: Moquegua, Tacna and Arequipa is carried out. The solar photovoltaic tariff has been obtained by auctions, between the year 2010 to 2016, these have decreased significantly until reaching network parity. The costs of both energy sources are compared.

Economic, Energy, and Environmental Analysis of PV with Battery Storage for Italian Households

The use of renewable energy sources is one way to decarbonize current energy consumption. In this context, photovoltaic (PV) technology plays a direct fundamental role since it can convert sun irradiance into electricity to be used for supplying electric loads for households. Despite the huge availability of the solar resource, the intermittence of PV production may reduce its exploitation. This problem can be solved by the introduction of storage systems, such as batteries, storing electricity when PV overproduction occurs and acting as a source when PV generation is absent. Consequently, increase in self-sufficiency and self-consumption can be expected in residential end users, paving the way for more sustainable energy systems. In this paper, an economic, energy, and environmental analysis of PV systems (without and with batteries) for the household is performed for the whole of Italy, by means of a Geographical Information Systems (GIS) approach. A model to simulate energy balance and to manage batteries is defined for households to assess the profitability of such systems under an Italian regulation framework. Concerning results, indicators are provided at a national scale using GIS tools to highlight areas where investments are more profitable, boosting the CO2 emission reduction.

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

This paper presents an agent-based model (ABM) for residential end-users, which is part of a larger, interdisciplinary co-simulation framework that helps to investigate the performance of future power distribution grids (i.e., smart grid scenarios). Different modes of governance (strong, soft and self-organization) as well as end-users’ heterogeneous behavior represent key influential factors. Feedback was implemented as a measure to foster grid-beneficial behavior, which encompasses a range of monetary and non-monetary incentives (e.g., via social comparison). The model of frame selection (MFS) serves as theoretical background for modelling end-users’ decision-making. Additionally, we conducted an online survey to ground the end-user sub-model on empirical data. Despite these empirical and theoretical foundations, the model presented should be viewed as a conceptual framework, which requires further data collection. Using an example scenario, representing a lowly populated residential area (167 households) with a high share of photovoltaic systems (30%), different modes of governance were compared with regard to their suitability for improving system stability (measured in cumulated load). Both soft and strong control were able to decrease overall fluctuations as well as the mean cumulated load (by approx. 10%, based on weekly observation). However, we argue that soft control could be sufficient and more societally desirable.

Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Sector coupling technologies in gas, electricity, and heat networks

Current investment in distribution networks for electricity, gas, and heat is high, and the distribution networks play a prominent role in the necessary transformation of the energy system. This paper provides insights into the relationship between residential end-user decisions on heat supply and their effect on infrastructure planning. Therefore, the gas, electricity, and heat networks are analyzed together. After a review of the characteristics of the networks, the most common sector coupling technologies are compared economically and environmentally. The results show that, under the assumptions made, heat pumps are the cheapest option for residential end-users in the long run. This raises the question of whether a parallel development of three different infrastructures for the heat supply of buildings is the best path to a successful energy transition.

Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response

This study covers a decision-making model in which a multi-energy system (MES) including heat pumps (HPs), combined heat and power (CHP), community energy storage (CES), air conditioners (ACs), and renewable energy sources (RESs) meets the electrical, cooling and heating demands of end-users in a smart neighborhood (SN). The thermostat set point control mechanism (TSCM) and direct compressor control mechanism (DCCM) based thermostatically controllable loads oriented demand response (DR) approaches are also considered in order to increase the effectiveness and economy of the MES operation. The SN, including houses with different types of residential end-users, has flexible and inelastic electricity, heating, and cooling power demands. CHPs, HPs, and ACs are operated optimally to keep the room temperatures between desired temperature limits; furthermore, some end-users have electric vehicles (EVs) assumed as flexible loads. Due to the intermittent nature of RESs, stochastic modeling is used to cope with uncertainties in their production pattern. In addition, time-of-use (TOU) electricity prices and real gas price data are used to handle the test system more realistically. Various comparative case studies have been conducted to prove the effectiveness of the proposed model. According to the obtained results, it can be stated that the DR strategies provide better results than the CES, and the most effective element in MES architecture is CHP for this study. Also, another striking finding is that the reduction in cost is experienced when RESs and EVs penetrate together.

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

This article proposes a novel Stackelberg game approach for activating demand response (DR) program in a residential area aiming at addressing the mismatch between the demand and renewable energy generation. In this study, two major players, namely the aggregator as a leader and the consumers as followers, are considered. The aggregator, which owns a wind farm and also receives power from the independent system operator (ISO), strives to obtain the maximum matching between the consumers' demand and forecasted wind power by incentivizing consumers to adjust their load through offering a bonus to them. On the other hand, consumers change their load profiles for obtaining the highest amount of bonuses. Each consumer has two kinds of loads including critical loads, which must be maintained under any circumstances, and the flexible loads, e.g., heating, ventilation, and air conditioning (HVAC) system, which can be regulated for DR purposes. In order to consider the uncertainty associated with the wind generation and the demands, a scenario-based stochastic programming model has been adopted in this work. Results show the effectiveness of the Stackelberg game model used for interaction between the aggregator and consumers, and the best response that can be served to both of them.

Economic potential of PV for Italian residential end-users

The installation of PV systems in the Italian residential sector represents the main component within the overall PV market. Nevertheless, a strong contraction of PV investment can be observed in Italy during the last period due to the closure of the feed-in-tariff mechanism. For this reason, new opportunities have been introduced by the Italian Government to support investment in the residential sector: net-metering, tax deduction and a novel regulatory scheme where PV production can be exchanged between an end-user and an energy-provider free of network charges.In this context, an economic assessment for PV installations in residential households is presented in this paper considering the benefits derived by the combination of these supporting schemes presently available in Italy. Increasing electricity consumptions of residential end-user are considered to explore the feasibility of PV installation in different configurations. Geographical information system is used here as a supporting tool to take into account the variation of solar radiation resource across the country, since economic results are influenced by the match between energy consumption and PV production. The results highlight how the perspective of PV investment is still positive even if a feed-in-tariff for PV production is no longer available.

Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications

Recently, hybrid multi-energy systems consisting of multiple generation, conversion, and storage technologies have been receiving great attention as a promising option to meet the multi-energy demands of residential end-users, by transforming them from passive consumers to active prosumers, who both produce and consume energy. The design problem of such systems is challenging due to the large number of degrees of freedom in the design and operation phases, so the system as a whole must be optimized. Moreover, both economic and low-carbon priorities should be considered in the design problem to foster an effective implementation and deployment. The aim of this paper was to present a methodology for the optimal design of multi-energy nanogrids (MENs) operating in grid-connected and islanded modes. Based on a pre-defined MEN superstructure, a multi-objective linear problem was established to find the types and sizes of the technologies in the MEN, with the aim to reduce the total annual cost and the fossil primary energy input, while satisfying the assigned time-varying user multi-energy demand. With reference to the latter, the thermal behavior of the building was simulated by using the dynamic simulation software TRNSYS. The Pareto frontier was found by minimizing a weighted sum of the total annual cost and fossil primary energy input, and the problem was solved by using branch-and-cut. In the numerical testing, a single-family house of 200 m2 located in Italy was considered as the residential end-user. Results show the effectiveness of the model for providing good balancing solutions for end-users based on economic and energetic priorities. Moreover, it was found that the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the configuration operating in islanded mode.

Using reinforcement learning for maximizing residential self-consumption – Results from a field test

This paper presents the results from a real residential field test in which one of the objectives was to maximize the instantaneous self-consumption of the local photovoltaic production. The field test was part of the REnnovates project and was conducted in different phases on houses in several residential districts located in Soesterberg, Heerhugowaard, Woerden and Soest, the Netherlands. To maximize self-consumption, buffered heat pump installations for domestic hot water and stationary residential battery systems were chosen due to their respective thermal and electrical storage capacities. The algorithm used to tackle the associated sequential decision-making problem was model-based reinforcement learning. The proposed algorithm learns the stochastic occupant behavior, uses predictions of local photovoltaic production and considers the dynamics of the system. The results show that this algorithm increased the average self-consumption percentage of the local PV generation (used instantaneously in situ) on average by 14%, even if only buffered heat pump installations for domestic hot water were used. This increase was achieved without causing any perceived discomfort to the residential end users. The average energy shifted per day from the solar production period to the night by the 2 kW/3.6 kWh batteries was 1.5 kWh. The main contribution of this work was therefore the real field implementation of the proposed algorithm. The results demonstrate that it is possible to improve even further the integration of local production using flexible loads.

Distributed Optimization Algorithm for Residential Flexibility Activation—Results From a Field Test

This paper presents results from a field test that uses a method for transactive control based on dual decomposition. The proposed approach is used to activate flexibility at the residential level to resolve any forecasted bottlenecks and power surpluses in the grid. The field test is part of the REnnovates project. The capability to react to grid requests comes from residential loads with some type of energy consumption flexibility associated. In the field test, three types of actors are included: the distribution system operator (DSO), the aggregator, and the residential end users. Two levels of control are distinguished: the end users’ level and the district level (grid congestion management). Dual decomposition is used to compute virtual prices that influence the power profile of individual houses to solve grid congestion problems, previously identified by the DSO, in the least intrusive or disturbing way. Results have shown that, even as operational constraints and end users’ comfort requirements limit the flexibility, congestions in the grid can be suppressed in most of the tests using the proposed method. This success is however dependent on the amount of energy requested to be changed, the corresponding period of the day, and for how long.

Influence of hydrogen addition to pipeline natural gas on the combustion performance of a cooktop burner

Displacing pipeline natural gas with renewable hydrogen is a promising way to reduce the emission of carbon dioxide, which is a major greenhouse gas. However, due to significantly differing characteristics of hydrogen and natural gas, such as flame speed, adiabatic flame temperature and stability limits, the combustion performance of hydrogen/natural gas mixture differs from pure natural gas. From the perspective of residential end users, a key question is: how much hydrogen can be injected into the pipeline natural gas without influencing the performance of the residential burners? A representative cooktop burner is selected to study the influence of hydrogen addition on the combustion and cooking performance. Flashback limits, ignition time, flame characteristics, cooking performance, combustion noise, burner temperature, and various emissions (NO, NO2, N2O, CO, unburned hydrocarbon (UHC), NH3) are evaluated for different levels of hydrogen addition. According to the experimental results, the combustion performance of the cooktop burner is not significantly affected with up to about 15% hydrogen addition by volume, which shows the feasibility of utilizing hydrogen on existing cooking appliances without any modification. The experiment methodologies and results in this study will serve as a reference for future test and emission regulation standards on domestic burners.

Do power sector reforms affect electricity prices in selected Asian countries?

This paper examines the impact of power sector reforms on electricity prices using panel data for selected Asian countries over the period 1970–2017. We estimate two separate models for domestic and industrial prices. Our results from pooled OLS and the random effect model show that the impact of independent power producers, privatisation, restructuring, and deregulation on electricity prices is negative and significant, making both domestic and industrial consumers better off. However, the welfare effects of unbundling generation from transmission and distribution appear to be beneficial for residential end-users only. Our results also indicate that other reform indicators including third-party access and the existence of independent regulatory agencies may not be the right choice for a reduction in electricity prices, given the absence of a requisite conducive environment for regulations in our sample countries. Thus, the inconsistent behaviour of different regulatory reforms towards different countries and regions of the world needs to be considered in the policy formulation of the power sector. We also note that the results can be magnified in terms of significance with the exclusion of some outlier observations in the data.

Optimal battery management for vehicle-to-home and vehicle-to-grid operations in a residential case study

In the mobility sector Electric Vehicles represent one of the main opportunities to ensure strong reduction of local pollution. However, their higher costs compared to gas-fuelled cars are still a barrier for their large diffusions. One possible solutions to increase EVs penetration is their use as storage within households equipped with Renewable Energy Sources enabling a flexible energy management, for instance by the Vehicle-to-Grid and/or Vehicle-to-Home scheme.The aim of this paper is to investigate possible management of the EV battery through an optimization approach capable to minimize the electricity supply costs for an Italian residential end-user with PV, considering battery constraints, such as driving habits. A statistical approach of driver behavior is integrated within the optimization approach to define some possible daily driving patterns. The optimization takes into consideration mobility needs, prices for selling and purchasing electricity and hourly electrical, heating and cooling load profiles of the household. On the basis of these constraints the optimizer identifies when PV overproduction can be either used to charge batteries or to partially cover the load demand of the household. Finally, economic and energy evaluations are performed under Monte Carlo simulations to highlight reliability of potential benefits for the household case study.

Price-responsive model predictive control of floor heating systems for demand response using building thermal mass

Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.