Residential End-users Research Articles

Residential PV-battery scheduling with stochastic optimization and neural network-driven scenario generation

Residential end-users have increasingly been attracted to installing behind-the-meter (BTM) photovoltaic (PV) and battery energy storage systems (BESS). The inherent stochastic nature of PV introduces challenges to energy management for PV-BESS owners. This work presents a two-stage stochastic optimization (SO) framework for battery operation that takes into account uncertainties in PV outputs to minimize daily grid consumption and battery degradation. A combination of a feed-forward neural network (FFNN) and an error analysis statistical technique is proposed to generate a highly accurate scenario set for estimating PV behavior. Then, the scenario set is reduced using a backward scenario reduction technique to address the issue of dimensionality that SO suffers from. The reduced scenario set is then fed into the two-stage SO model to calculate expected electricity costs and battery degradation for the day-ahead PV-BESS problem. The suggested model's performance in controlling BTM batteries is evaluated using real-world data collected from smart meters installed in a house in Aran Islands, Ireland. The results show that the model can effectively withstand sudden changes in the PV output and generate results that are comparable to an ideal case with accurate PV data available.

Achieving Techno-Economic Feasibility for Hybrid Renewable Energy Systems through the Production of Energy and Alternative Fuels

In developing countries like Ghana, the conversion of waste into energy is gaining greater interest among policy makers and researchers. The present study investigates the feasibility of producing electricity and/or fuels from a hybrid waste-to-energy pilot plant located in the Ashanti Region of Ghana. The plant integrates three technologies: anaerobic digestion, pyrolysis and solar PV. The plant has the potential to produce both energy and fuels such as green hydrogen, refuse derived fuels, bio-compressed natural gas and compost. Thus, this study compares the financial feasibility of three scenarios—generating electricity and fuels, generating electricity alone and generating fuels alone—by modelling their energy output and financial performance using RETSCREEN expert 6.0.7.55 and Microsoft Excel 2019 softwares. The results indicate that the multiple products of electricity and fuels provide higher investment interest with a Net Present Value in excess of EUR 13 million and a payback period of 12 years compared to the electricity-only model. Also, converting electricity into fuels alone also provides substantial benefits which can be explored. However, the Levelized Cost of Energy, ranging from 0.3 to 0.68 EUR/kWh, is far above the average residential End User tariff. Overall, this study provides an important methodology for assessing the potential products of future projects.

Enhancing Transactive Energy Trading Framework for Residential End Users

Enhancing Transactive Energy Trading Framework for Residential End Users

Rebuttal to the Correspondence on Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User.

Rebuttal to the Correspondence on Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User.

Correspondence on "Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User".

Correspondence on "Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User".

Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The increasing complexity of multi-energy coordinated microgrids presents a challenge for traditional demand response providers to adapt to end users’ multi-energy interactions. The primary aim of demand response providers is to maximize their total profits via designing a pricing strategy for end users. The main challenge lies in the fact that DRPs have no access to the end users’ private preferences. To address this challenge, we propose a deep reinforcement learning-based approach to devise a coordinated scheduling and pricing strategy without requiring any private information. First, we develop an integrated scheduling model that combines power and gas demand response by converting multiple energy sources with different types of residential end users. Then, we formulate the pricing strategy as a Markov Decision Process with an unknown transition. The novel soft actor-critic algorithm is utilized to efficiently train neural networks with the entropy function and to learn the pricing strategies to maximize demand response providers’ profits under various sources of uncertainties. Case studies are conducted to demonstrate the effectiveness of the proposed approach in both deterministic and stochastic environment settings. Our proposed approach is also shown to be effective in handling different levels of uncertainties and achieving the near-optimal pricing strategy.

Impact of policies on residential multi-energy systems for consumers and prosumers

Multi-energy systems can improve the performance of traditional energy systems, where energy carriers and sectors are decoupled, in terms of economic, environmental, and social sustainability, measured as the total cost of energy, emissions per energy demand, and self-sufficiency, respectively. This study assesses the impact that policy mechanisms can have in enabling these sustainability benefits. A mixed-integer linear problem is implemented, which optimizes the design and operation of multi-energy systems to minimize the total annual cost of supplying energy to residential end-users. Four policy types are tested for a Swiss case study, namely a feed-in tariff, an investment support mechanism, a carbon tax, and a regulation-based carbon cap. To assess how the policy impact varies between different end-users, we distinguish between passive consumers, that cannot access subsidies, and prosumers, who can. In our case study, subsidies, such as a feed-in tariff and an investment support mechanism, decrease the cost of energy for prosumers by up to 10%, but increase the cost for consumers by up to 33%, which points to the need of including energy equity considerations when designing policies. The carbon cap and the carbon tax impact all end-users equally, and tend to perform better in terms of reducing emissions. Emission reductions of up to 60% and 39% are observed for the carbon cap and carbon tax, respectively. The feed-in tariff and carbon cap perform best in fostering self-sufficiency and achieve balanced energy autonomy for high policy levels, revealing a trade-off between the different sustainability dimensions.

Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.

Analysis of individual natural gas consumption and price elasticity: Evidence from billing data in Italy

The price elasticity of residential and non-residential energy demand is crucial for assessing energy policies and consumption forecasts.In this paper, using the monthly billing data of 51,177 end-users in Veneto (Italy), we implemented two panel-data models, analyzing the elasticity between natural gas demand and price variations. The models differ on how the possible endogeneity between price and demand is treated: (1) the total natural gas price is split into several components and we study only the natural gas price part free of endogeneity; (2) instrumental variable methods are used, considering two external instruments to regress the natural gas price from exogenous sources.We also investigated consumption patterns, underlining the influence of weather factors, market- liberalization, smart-metering technologies and macro-socioeconomic trends. The principal contribution of this paper is twofold: firstly, to supplement the slight empirical literature on natural gas consumption and price elasticity at the micro-level, based on the Italian market, providing a new contribution helpful in defining energy and environmental policies in Italy. Secondly, we provide an empirical method for utilities to exploit information that is often untapped such as billing invoices and technical data.The results show that residential end-users are more reactive to price changes than non-residents. However, the price elasticities found in this analysis are less than 1% for both groups, supporting outcomes found in other countries, i.e., natural gas consumers slightly react to price variations.

Options for change: Restructuring California’s residential inclining rates for a better electricity future

Local distribution companies (LDCs) regulated by the California Public Utilities Commission use mandatory inclining rates (MIR) to bill their residential electricity customers. However, MIR hinder the California electricity industry’s provision of clean and reliable service at affordable rates. Hence, we consider the following options to restructure MIR: (1) adopting a flat energy rate and revised customer charges; (2) replacing the flat rate in (1) with time-of-use (TOU) rates; (3) replacing the TOU rates in (2) with real-time pricing; (4) replacing (3) with a Hopkinson tariff with reliability differentiation. Our assessment of these options recommends sequential implementation (1), (2) and (4), thus lessening the potentially large and adverse bill impacts on some residential end-users caused by a drastic rate design change. Our recommended implementation sequence is (2) and (4) when the potentially large bill impacts of TOU pricing on the small and poor end-users with relatively high peak kWh consumption are to be mitigated by income-based customer charges.

Decarbonizing residential energy consumption under the Italian collective self-consumption regulation

The recent Italian regulatory framework is promoting Collective Self-Consumption to play a key role in the energy transition. In fact, this new scheme allows sharing and exchange of electricity produced from Renewable Energy Sources (RES) among different end-users living in the same multi-family building block. In this context, this paper aims to perform an energy, economic and environmental assessment of creating a RES-based energy community formed by end-users located in the same residential building, taking into account the currently available incentive schemes. In particular, two different progressive scenarios have been analysed: the first one includes only a photovoltaic system to supply the aggregated electricity demand of the apartments, while a heat pump is further integrated in the second scenario for supplying and electrifying/decarbonizing also the space heating demand of the building. Available temperature and solar irradiance datasets at national level were then used to spread the analysis to the whole country, at different latitudes. Although differences exist at regional levels for the proposed scenarios, the results highlight how the RES-based Collective Self-Consumption scheme is economically profitable for Italian residential end-users with cost savings up to 32% and environmentally sustainable with carbon emissions reduction up to 60%. • Economic, energy and environmental analysis of PV and HP for Italian households. • Energy modeling considering solar radiation and air temperature variability with GIS. • The results highlight how the economic and environmental perspectives are positive. • The new regulatory framework concerning energy sharing improve RES diffusion.

Cost optimization of a microgrid considering vehicle-to-grid technology and demand response

Demand response (DR) programs can offer various benefits especially in microgrid environments with renewable energy systems (RESs) and energy storage technologies when effectively planned and managed. Accordingly, this study proposes an energy management approach for a neighborhood including residential end-users with photovoltaic (PV) systems, a shared energy storage system (ESS) and an electric vehicle (EV) fleet. The proposed approach presents a novel energy credit mechanism (ECM) for the EV fleet and households separately to exploit the EV batteries and store the excess PV energy in the neighborhood through the shared ESS for later use. End-users gain energy credits before a DR event and use these credits during the peak periods to minimize their total energy cost (TEC), resulted in a decrease in the peak demand. Also, the energy credits gained by the EV fleet are used through the vehicle-to-home (V2H) and vehicle-to-grid (V2G) services with the same objective. In order to conduct a more realistic analysis, a battery degradation cost estimation model is employed and the uncertain behavior of the EV users is considered. The case studies show that the proposed optimization strategy has the capability of considerably reducing the energy costs and peak demand.

Resiliency-Sensitive Decision Making Mechanism for a Residential Community Enhanced with Bi-Directional Operation of Fuel Cell Electric Vehicles

The trend regarding providing more distributed solutions compared to a fully centralized operation has increased the research activities conducted on the improvement of active regional communities in the power system operation in the last decades. In this study, an energy management-oriented decision-making mechanism for residential end-users based local community is proposed in a mixed-integer linear programming context. The proposed concept normally includes inflexible resiliency-sensitive load–demand activated as flexible during abnormal operating conditions, fuel cell electric vehicles (FCEVs) fed via the hydrogen provided by an electrolyzer unit connected to the residential community and capable of acting in vehicle-to-grid (V2G) mode, common energy storage and photovoltaic (PV) based distributed generation units and dispersed PV based generating options at the end-user premises. The combination of the hydrogen–electricity chain with the V2G capability of FCEVs and the resiliency-sensitive loads together with common ESS and generation units provides the novelty the study brings to the existing literature. The concept was tested under different case studies also with different objective functions.

Analysis of single- and multi-family residential electricity consumption in a large urban environment: Evidence from Chicago, IL

Natural and human-caused extreme events can alter residential electricity demand in urban areas and stress the electricity grid, with different types of residential electricity consumers exhibiting different consumption patterns. Residential electricity demands have been widely analyzed considering single-family consumers; however, multi-family consumption patterns remain comparatively understudied. The deployment of smart electricity meters enables the identification of single- and multi-family residential electricity consumption patterns at high temporal resolution. Using smart electricity meter data for the greater Chicago area, we compare electricity demand profiles reported by smart meters from single- and multi-family consumers in a large and diverse urban environment to understand residential electricity patterns better. Our study comprehensively analyzes the daily electricity demand profiles of these two types of residential consumers to identify peak electricity consumption times and magnitudes. Results show that the electricity demand of both residential end-users follows similar time of use patterns, and single-family users approximately double the demand of multi-family users on a per household basis. We also present predictive models of the electricity demand with socioeconomic data at the zip code level. Predictive model results show that multiple linear regression models explain up to 62% and 41% of the mean daily electricity (MDE) demand of single- and multi-family users, respectively. The median age of occupants, percent age 65 and older, mean commute time, and percent high school or higher education are statistically significant predictors of the MDE demand of single-family users, with percent high school or higher education having the highest relative importance. Similarly, median building age, percent multi-family, percent female, median age of occupants, and mean commute time are statistically significant predictors of multi-family electricity consumption, with median age of occupants having the highest relative importance. Modeling electricity demand to uncover differences between single- and multi-family residential electricity demands can assist city planners and utility managers to develop tailored demand management strategies.

Experimental Investigation into Liquid and Solid Separation by an Industrial Mesh-Vane-Type Separator on Natural Gas at 4–5 MPa, Different Liquid Loadings and Gas Flows

AbstractInline vertical separators are commonly employed in natural gas transmission facilities (e.g., receipt stations) to filter liquid contaminants such as compressor oil, glycol, and free water from the gas stream. Manufacturers of these separators have claimed liquid removal efficiencies of 98–99% for droplet sizes ≥ 8 μm. However, these contaminants have been found invariably in piping systems downstream of these separators, suggesting inadequate separator performance. Currently, there is a lack of ability to verify manufacturer claims due to difficulties in quantifying liquid contaminants and droplet characteristics. The potential consequences of having such contaminants include lower gas quality, impaired gas metering accuracy, corrosion and damage to equipment/instrumentation, and adverse impacts on industrial and residential end users. This paper presents performance testing of a mesh-vane-type vertical separator conducted on high pressure, pipeline quality, natural gas in the range of 4–5 MPa and flow velocity in the range of 1.3–13 m/s in the DN150 separator inlet (hence turndown ratio of 10:1). Four different spray nozzles were used to inject and atomize industrial compressor oil with a liquid-to-gas mass loading ratio of 0.06–1.8%. Test results revealed that the average bulk efficiency separation performance for this separator is approximately 90.34%. This was found to be independent of the liquid-to-gas mass loading ratio. The effective Souders–Brown K-factor was found to be 0.15 m/s. This is below literature published data in the range of 0.27–0.3 m/s for horizontal flow, vane-pack-type separators. Liquid separation tests were also conducted following the injection of solid glass beads. Liquid separation efficiency decreased by approximately 9%, following the injection of 7 kg of industrial glass beads over a period of approximately 4 h. This was attributed to accumulation of solids in the vane pack.

An optimization model to characterize the aggregated flexibility responsiveness of residential end-users

This paper proposes an optimization model to characterize the aggregated flexibility responsiveness of residential end-users. The outcomes of this study are aimed to support the aggregator in defining the bids to the reserve services markets. To this end, the operation of demand-side energy resources of different end-users is modeled and optimized. The operation scheduling is defined in face of the time-differentiated prices defined by the energy supplier. The aggregator defines a reward that is communicated to the end-users. The flexibility of each end-user is calculated by reoptimizing its scheduling problem affected by the reward. Another optimization model is formulated at the aggregator level that collects the optimal flexibility of all individual end-users. The amount of aggregated flexibility is computed under two different aggregation scenarios to evaluate the effectiveness of the proposed methodology. The impact of the reward on the end-users’ responsiveness and the aggregator’s profit are assessed considering different reward values and timeframe of the market. The illustrative results show the effectiveness of the proposed model in characterizing the flexibility at both end-user and aggregator level.

Integrating flexibility provision into operation planning: A generic framework to assess potentials and bid prices of end-users

End-user flexibility is an essential resource for decarbonized energy systems, and can be exploited via distributed flexibility markets. To participate, end-users must weigh potential revenues against their primary objectives, subject to their operational constraints. This work presents a technology-neutral framework to simultaneously determine the optimal operation, flexibility bids and prices by extending original operation planning problems into two-stage stochastic ones. Recovery constraints are introduced to exclude potentially adverse bids. As case studies, diverse residential end-users, who minimize their cost and additionally place flexibility bids, are analyzed. The results reveal complex time-varying and system-specific dynamics of bid volumes and prices. For example, heating systems receiving flat electricity rates can effortlessly bid flexibility from forcing early heat generation, namely, positive flexibility for combined heat and power units and negative flexibility for heat pumps. However, under time-varying rates, bid patterns change and become synchronized, which can cause scarcity in markets with homogeneous participants. With this framework, end-users can assess technical and economic potentials, plan their operations and market participation. As operation planning also reckons with the expected flexibility demand of the system, bids in time of need are likely higher. Lastly, detailed potential assessments also aid operators in designing flexibility portfolios or markets.

Comment on "Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User".

Comment on "Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User".

Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User.

The presence of volatile organic compounds (VOCs) in unprocessed natural gas (NG) is well documented; however, the degree to which VOCs are present in NG at the point of end use is largely uncharacterized. We collected 234 whole NG samples across 69 unique residential locations across the Greater Boston metropolitan area, Massachusetts. NG samples were measured for methane (CH4), ethane (C2H6), and nonmethane VOC (NMVOC) content (including tentatively identified compounds) using commercially available USEPA analytical methods. Results revealed 296 unique NMVOC constituents in end use NG, of which 21 (or approximately 7%) were designated as hazardous air pollutants. Benzene (bootstrapped mean = 164 ppbv; SD = 16; 95% CI: 134–196) was detected in 95% of samples along with hexane (98% detection), toluene (94%), heptane (94%), and cyclohexane (89%), contributing to a mean total concentration of NMVOCs in distribution-grade NG of 6.0 ppmv (95% CI: 5.5–6.6). While total VOCs exhibited significant spatial variability, over twice as much temporal variability was observed, with a wintertime NG benzene concentration nearly eight-fold greater than summertime. By using previous NG leakage data, we estimated that 120–356 kg/yr of annual NG benzene emissions throughout Greater Boston are not currently accounted for in emissions inventories, along with an unaccounted-for indoor portion. NG-odorant content (tert-butyl mercaptan and isopropyl mercaptan) was used to estimate that a mean NG-CH4 concentration of 21.3 ppmv (95% CI: 16.7–25.9) could persist undetected in ambient air given known odor detection thresholds. This implies that indoor NG leakage may be an underappreciated source of both CH4 and associated VOCs.

Uncertainty-aware non-supplied load minimization oriented demand response program for PV/FC power system with electrolyzer back-up

This manuscript presents robust optimization framework with multi-objective approach for day-ahead operation of hybrid power system consisted of photovoltaic (PV) panel, fuel cell (FC) with electrolyzer (Elc) back-up. The multi-objective optimization model of PV/FC/Elc has been developed by combining the physical and operational constraints with mathematical equivalence of affine forms as well as considering the uncertainties occurring in the load demand and PV power generation. As a novel contribution to the literature, three robust optimization approaches for scheduling the hybrid power systems have been presented through the conservatism approach. Moreover, the objective function has been described to integrate the different objectives as aiming to minimize non-supplied load, while maximize the utilization rate of the essential source. By this way, it has been aimed to accelerate the adaptation of the demand response strategies for the proposed system. Moreover, this hybrid scheme provides solutions to meet the power demands for the residential end-users located in off-grid areas or islands. The mixed integer linear programming (MILP) model is tested in GAMS v.24.1.3 using the solver CPLEX v.12 and the developed strategy is analyzed for different load patterns. The results are discussed and commented with some suggestions to prove the contribution of the paper. • Uncertainty representation in load and power profile is provided. • Multi-objective robust optimization of household DR program for off-grid or island mode applications is presented. • Case study based analysis of optimal management of PV/FC power system with Elc back-up is carried out. • A compact and flexible MILP based modelling of PV/FC/Elc hybrid power system is offered. • Fulfilling the comfort and efficient operation requirements in the same optimization problem is performed.