Energy Prosumers Research Articles

Modern Use of Prosumer Energy Regulation Capabilities for the Provision of Microgrid Flexibility Services

Due to the prospect of climate change and the challenges posed by the European Union to the modern power grid, a decentralized system based on distributed energy sources is being created from a centralized system based on utility power. It also involves new ideas on the operation and management of power grids, especially at the level of low-voltage distribution networks, where prosumers play a special role. In addition to the transformation of sources to renewables, the aim is to increase the flexibility of power grids by exploiting the regulatory potential of flexible grid components. The issue of grid flexibility assumes particular importance in the case of microgrids and local grids covered by the energy communities. Many posts describe the realization of the task of flexibility through energy storage, e.g., storing storage resources in electric vehicles or the use of energy transformation through conversion to heat, air compression air, or process cooling. However, there seems to be a lack of exploration of the topic, where the photovoltaic inverter could provide a flexible energy source while maintaining the rigor of power quality. This article presents current developments in low-voltage grids and the prospect of using prosumer installations to provide grid flexibility and stability.

Blockchain-Based Distributed Federated Learning in Smart Grid

The participation of prosumers in demand-response programs is essential for the success of demand-side management in renewable-powered energy grids. Unfortunately, the engagement is still low due to concerns related to the privacy of their energy data used in the prediction processes. In this paper, we propose a blockchain-based distributed federated learning (FL) technique for energy-demand prediction that combines FL with blockchain to provide data privacy and trust features for energy prosumers. The privacy-sensitive energy data are stored locally at edge prosumer nodes without revealing it to third parties, with only the learned local model weights being shared using a blockchain network. The global federated model is not centralized but distributed and replicated over the blockchain overlay, ensuring the model immutability and provenance of parameter updates. We had proposed smart contracts to deal with the integration of local machine-learning prediction models with the blockchain, defining functions for the model parameters’ scaling and reduction of blockchain overhead. The centralized, local-edge, and blockchain-integrated models are comparatively evaluated for prediction of energy demand 24 h ahead using a multi-layer perceptron model and the monitored energy data of several prosumers. The results show only a slight decrease in prediction accuracy in the case of blockchain-based distributed FL with reliable data privacy support compared with the centralized learning solution.

The Use of Solar Energy by Households and Energy Cooperatives in Post-War Ukraine: Lessons Learned from Austria

The Russia-Ukraine war of 2022 showed the danger of reliance on autocratic regimes for energy supply. The use of renewables is a viable opportunity to substitute energy imports. Prosumption is one of the essential pillars of the Fourth Energy Package of the EU. After the war, Ukraine will require recovery packages, among other policies, aimed at increased energy prosumption. It will spur energy generation and foster employment, as well as being one of the means to avoid CO2 emissions. Following the example of Austria, the investment grant sizes for households and energy cooperatives in Ukraine were assessed. Potential job growth was estimated using the employment factor method and the size of CO2 emissions reduction. Should investment grants of EUR 50–200/kW for households and energy cooperatives be introduced, up to EUR 359.4 million would be required in 2022–2030. Households and energy cooperatives in Ukraine will still need to invest up to EUR 988 million until 2030. About 11 thousand full-time jobs may appear by 2030. Energy generation by households in 2020–2030 would enable the avoidance of 3.39–5.94 million tons of CO2 emissions.

Sizing of Rooftop PV Array and Community-Run Battery Storage for an Energy Cooperative in Prosumer Cluster

A standalone system can address the problem of uncovered electricity from the grid. The cost of energy storage for installing renewable energy systems is one of the issues of such a system. This paper introduces and investigates the optimal capacity of a novel energy cooperative system with prosumer clusters and a community battery bank as typical energy storage. The system’s function is formulated to minimize the investor’s annual expenditure. The proposed energy cooperative system uses actual annual solar insolation data and the electric load demand of houses in the optimization process. The model, as mentioned above, is applied to two system configurations – energy cooperative without and with Prosumer to Prosumer (P2P) energy sharing. The reliability factor Loss of Power Supply Probability (LPSP) from the Cooperative Energy Sharing algorithm is taken as a constraint in the formulation. The comparison of the two configurations brings out the importance of P2P energy sharing in a standalone Energy Cooperative system. Particle Swarm Optimization (PSO) algorithm is used to achieve this optimization. The PSO results show that the proposed Energy Cooperative configurations are promising to facilitate the system’s reliability.

Three energy self-sufficient networks of wastewater treatment plants developed by nonlinear bi-level optimization models in Jeju Island

Classical consumer communities are gradually turning to energy prosumers who employ innovative and self-sufficient systems. Despite the importance of wastewater treatment plants (WWTPs) in environmental protection and public health, they still consume considerable energy. This study employs bi-level nonlinear optimization models and a systematic platform of four novel techniques to design an energy self-sufficient network of WWTPs on Jeju Island, South Korea. A systematic platform consisting of four novel techniques is employed for designing energy self-sufficient WWTPs. The four techniques are dehydration of generated biogas from an anaerobic digestion, a nonlinear thermo-mathematical program for maximizing the generated electricity per input sludge, a deficit-surplus trade to avoid storage usage, and a bi-level multi-integer nonlinear program for optimal sizing of photovoltaic (PV) arrays. Three self-sufficient designs (SSDs) are proposed, including a network of biogas-fed combined heat and power plants (CHPPs) coupled with WWTPs (SSD-1), solar-powered WWTPs (SSD-2), and hybrid CHPP WWTPs retrofitted with PV systems (SSD-3). The biogas obtained from anaerobic digestion of waste-activated sludge in the WWTPs is dehydrated to fuel the CHPPs that use the available grid as a storage unit to establish a self-sufficient network in SSD-1. The PV units substitute for the CHPPs in SSD-2, while the hybrid systems minimize the local power trade in SSD-3. Economic, social, and environmental models of the optimal configurations are developed to compare the proposed SSDs. The resulting life-cycle impacts (LCI) of the proposed designs vary between 2 and 4531 pts/day depending on the energy demand and SSDs of the WWTPs. When the energy demand is 155.1 kWh/day in SSD-2, the LCI is 2 pts/day, whereas it reaches 150 pts/day for the energy demand of 10,786.6 kWh/day. A maximum of 26,278 new jobs could be created at an annual cost of −1,345,458 USD in SSD-2. The solar-powered scenarios had lower environmental impacts and greater social benefits, but were more expensive, compared with anaerobic digestion-assisted designs. Gradually adding bio-energy to an energy mix using hybrid renewable networks can therefore facilitate a smooth transition to a carbon-free economy on Jeju Island by 2030.

Networked Energy Cooperation in Dual Powered Green Cellular Networks

Designing solar-enabled and power grid connected, ‘dual-powered’, cellular networks is challenging due to the double stochasticity arising from energy harvest and user traffic, resulting in spatio-temporally varying traffic-energy imbalances. Improper strategy to optimize the power grid connectivity results in generation of significant carbon footprint. In this paper, we present an analytical framework to mathematically capture the traffic-energy imbalances in such a dual-powered network and propose to improve the temporal network energy utilization by exploiting these imbalances through a cooperative energy sharing mechanism among the base stations (BSs), via the grid infrastructure itself. The cooperative communication system is designed and optimized independently from two perspectives, namely, grid energy procurement and carbon emission minimization (in carbon free ‘energy producer’ mode) and operator revenue maximization (in ‘energy prosumer’ mode). The energy producer mode involves the BSs, without the flexibility to procure energy and acting as distributed energy source to the power grid. The energy prosumer mode provides additional flexibility of grid energy procurement to the BSs in addition to energy sharing and selling. For a given capital expenditure (CAPEX), both the optimization problems are reformulated into convex quadratic problems and closed form expressions for the optimal quanta of energies to be shared/procured through/from the grid are obtained. The optimal CAPEX for the proposed modes of network operation are obtained via linear revenue maximization problem formulation. The results demonstrate that the proposed cooperative energy framework significantly improves the temporal network energy utilization, thereby reducing the grid energy procurement and providing significant revenue gains compared to the state-of-art.

Grid-friendly energy prosumers based on the energy router with load switching functionality

Huge numbers of residential energy prosumers (EPs) will be widely deployed in the next generation power system which is dominated by renewable energies. The intermittent and non-dispatchable natures of the renewable energies will inevitably bring sporadic and unpredictable disturbances to the power system, making “grid friendliness” an essential requirement for the EPs. To tackle this problem, this paper focuses on the hardware implementation of the energy router (ER) and corresponsive coordinated energy management strategy for grid friendly EPs. The coordinated energy management strategy, combined with a fuzzy logic controller (FLC) for energy storage (ES) unit control and an improved discrete binary particle swarm optimization (DBPSO) algorithm for load scheduling, is elaborated for embedded control of the ER by maintaining balance of the accuracy, efficiency and cost considerations. Built as the energy interface between the EP and the power system, the ER is facilitated with plug-and-play source/load/ES connections, intelligent monitor and control interfaces as well as a load switching module, enabling the functionalities of the coordinated energy management strategy to be implemented. Experimental verification shows that, the developed ER enhances the energy management flexibilities for the EP, and the presented FLC + DBPSO algorithm enables coordinated operation of the EP and potential benefits in renewable DG utilization by improving the performances of PV self-use ratio, load rate and energy cost of the EP system by about 20 %, 50 % and 20 %, respectively, while at the same time maintaining the EP system reliability and consumer comfort, as well as meeting the grid friendly requirements.

Prosuming – energy sufficiency and rebound effects: Climate impact of changing household consumption patterns in Germany

Changes in energy consumption patterns after becoming a prosumer household are rarely associated with negative environmental effects, as prosuming is intuitively assumed to be emission-reducing. This paper demonstrates the importance of sufficiency-oriented energy prosumer behavior for climate neutrality goals by quantifying GHG emissions for photovoltaic (PV) prosumers at the German household and energy system level. Based on the results, recommendations are derived for promoting energy sufficiency in prosumer households.

Renewable Energy and Poverty in Sustainable Development of the European Union

The current socioeconomic development results in a number of consequences that lead to changes in the environment. These changes are often harmful and are associated with over- or misuse of natural resources. The issue of sustainable development is increasingly taking a prominent place in regional and local development strategies. Access to energy services is essential for social inclusion. Addressing poverty, including energy poverty, can bring a number of benefits related to reduced health expenditures, reduced air pollution, improved comfort and well-being, and improved household budgets, among other things. According to the results of the analysis, the utilisation of energy from renewable sources is intrinsically linked with the salary level – on average, a higher salary level is accompanied by a larger scale of utilising energy from renewable sources. In spite of the expected negative value, a similar relationship has been observed in the case of correlating the level of poverty with the use of energy from renewable sources – it is higher when the use of energy from renewable sources is higher. The performed study indicates the lack of adequately constructed support mechanisms for the poor in terms of financing and operating installations generating green energy, as well as the lack of proper education as regards local and global benefits resulting from prosumer energy. The desire to eliminate the abovementioned barriers necessitates the continuation of actions concerning synergistic accomplishment of the first and seventh targets, constituting two out of seventeen foundations of sustainable development.

A Multi-Agent Adaptive Architecture for Smart-Grid-Intrusion Detection and Prevention

The present paper deals with selected aspects of energy prosumers’ security needs. The analysis reported aim to illustrate the concept of the implementation of intrusion-detection systems (IDS)/intrusion-prevention systems (IPS), as supporting agent systems for smart grids. The contribution proposes the architecture of an agent system aimed at collecting, processing, monitoring, and possibly reacting to changes in the smart grid. Furthermore, an algorithm is proposed to support the construction of a smart-grid-operating profile, based on a set of parameters describing the devices. Its application is presented in the example of data collected from the network, indicating the process of building a device-operation profile and a possible mechanism for detecting its changes. The proposed algorithm for building the operating profile of devices in the smart grid, based on the mechanism of continuous learning by the system, allows for detecting network malfunctions not only in terms of individual events but also regarding limits of the scope of system alerts, by determining the typical behavior of devices in the smart grid. The paper gives recommendations to a software-agent system development, which is dedicated to detecting and preventing anomalies in smart grids.

Multi-facility aggregate production planning with prosumer microgrid: A two-stage stochastic program

The U.S. industries accounted for 37% of the wind turbine capacity installed in 2020 and corporate solar purchases have totaled $22 billion over the last 10 years. However, affordability and intermittency of renewable generation are still the main obstacles for firms to power their operations using wind and solar energy. Aggregate production planning models that incorporate onsite renewable energy (APPMs-RE) are the state-of-the-art to facilitate renewable adoption. A large number of APPMs are available in the literature, but renewables-based APPMs are still inadequately investigated. This paper proposes a novel APPM-RE because it optimizes wide-ranging RE decisions such as microgrid capacity, energy storage, prosumer energy transactions, and demand response along with production, machine usage, and workforce levels to minimize cost. The model is a two-stage stochastic program considering uncertainties in product demand, labor and machine capacity, and power generation. The research goals are to (1) investigate the feasibility of decarbonizing the production, transportation, and warehousing operations, (2) examine the system affordability under practical hourly load requirements, (3) assess the cost-benefit of a prosumer energy transaction mechanism (PETM), (4) identify the effect in cost and microgrid capacity of time-of-use tariff, and (5) demonstrate the suitability of a two-stage stochastic programming model. The numerical experiments relying on climate analytics of six U.S. cities represent a broad scope of climate conditions. The managerial insights derived from this study are: (1) renewable microgrids with PETM may attain net-zero carbon operations with an affordable levelized cost of energy, and (2) time-of-use stimulates more adoption of solar generation than the flat rate.

Business Model Innovations for Renewable Energy Prosumer Development in Germany

In Germany, the number of renewable energy prosumers has increased rapidly since 2000. However, the development of prosumers has faced and will continue to face various economic, social, and technological challenges, which have triggered the emergence of a number of innovative business models (BM). This paper enriches the empirical basis for prosumer-oriented BMs by investigating two BM innovations in Germany (P2P electricity trading and aggregation of small-size prosumers) drawing on business model and socio-technical transition theories. A mix of qualitative data collection methods, including document analysis and semi-structured expert interviews, was applied. We found that while both BMs can potentially address the challenges associated with renewable energy prosumer development in Germany, small-scale prosumers’ participation in both BMs has been limited so far. We identified various internal and external drivers and barriers for scaling up these BMs for prosumer development in Germany. Despite these barriers, both aggregation and centralized P2P targeting prosumers may potentially be also taken up by incumbent market actors such as utilities. Decentralized P2P on the other hand still faces significant internal and external barriers for upscaling. Based on the analysis, the paper provides policy recommendations with respect to the identified drivers and barriers. From a theoretical perspective, our findings provide further evidence to challenge the dichotomous understanding of niche actors and incumbents, the latter of which are often theorized to be resistant to radical innovations.

IoT-Based Low-Voltage Power Distribution System Management and Control Platform

In order to improve the operational safety and market operation efficiency of the prosumer energy community, to achieve comprehensive monitoring of abnormalities, fault alarms, and intelligent control and maintenance, to reduce the risk of information security, and to address the many types of operational testing and metering equipment in the prosumer community, the duplication of functions and hardware composition was performed, resulting in the waste of resources of monitoring and metering equipment. In the meantime, we proposed an intelligent perception device-based IoT platform architecture for power distribution communities by integrating the software and hardware of the original operation monitoring and metering equipment of the prosumer-integrated communities. The intelligent perception device for community IoT sensing was first introduced, and then, the operation monitoring and metering equipment in the distribution station area was integrated and optimized to enhance the panoramic state sensing capability of the intelligent terminal; the expansion application direction of data-driven distribution IoT was proposed from the typical application scenario of the terminal.

Self-sufficiency and charger constraints of prosumer households with vehicle-to-home strategies

In recent years, the market of electric vehicles has been growing strongly. This growth is accompanied by discussions on vehicle-to-home strategies that allow households with a photovoltaic system and an electric vehicle both to charge the vehicle with solar energy and to supply energy from the vehicle to the household. However, vehicle-to-home technology is still not yet widely implemented in prosumer households and there is still little literature about the impact of technological constraints given by the hardware and charging protocols on prosumer energy consumption. To close this research gap, we develop heuristic vehicle-to-home charging strategies that aim to increase self-sufficiency, vehicle availability and traction battery lifetime. We discuss charging power constraints due to technical limitations measured in the laboratory and communication protocols. We investigate the impact of charging power constraints, bidirectional charger capability and forecasting algorithms on the self-sufficiency of the prosumer household. The simulation model integrates a comprehensive electric vehicle model, photovoltaic system model and historic measurement data of prosumer and driving profiles. We propose and simulate three different exemplary mobility profile scenarios. The mobility scenarios differ in their departure and arrival time distributions and are named Worker, Half-time Worker and Late Worker. The developed smart charging strategies can increase the self-sufficiency of the household by up to 16.9 percentage points in comparison to charging the vehicle with maximum power upon plug-in. Decreasing the minimum charging power constraint from 4.1kW to 1.8kW can increase self-sufficiency by up to 10.5 percentage points. Smart charging strategies, the use of a bidirectional charger, relaxation of charging power constraints and the use of forecasting algorithms increase the self-sufficiency of a prosumer household with a photovoltaic system and an electric vehicle.

Residential energy management system based on integration of fuzzy logic and simulated annealing

With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios by consumer behavior. In this study, an intelligent residential energy management architecture and algorithm to manage residential energy production/consumption are proposed. The algorithm controls the energy flow in the home according to real-time potential solar power estimation, demanded energy estimation, electricity consumption price, and battery state-of-charge (SoC). The fuzzy logic algorithm has been developed to determine the estimated comfort and cost-effectiveness ratios in the near future. The simulated annealing algorithm, a meta-heuristic algorithm, is performed to obtain the best operating point decision of the battery using the comfort and cost-effectiveness ratios. Energy flow direction and battery SoC are optimized using simulated annealing based on the comfort and cost-effectiveness ratio (comparison of alternatives with respect to multiple criteria of different levels of importance for energy usage). The focus is to generate maximum profit from energy sales for monthly profit to be achieved. Prototyped hardware and software are implemented and tested in real-time. The test results show that the 20% reduces energy consumption, and a monthly gain of $89.2 is obtained from energy sales using the proposed method. Therefore, the test results reveal the effectiveness of the proposed architecture and algorithm.

A Comparative Study on Voltage Level Standard for DC Residential Power Systems

With the development of dc renewable power generation and dc power consumption, more and more attention has been paid to residential low-voltage dc (RLVDC) power systems in recent years because of their advantages in conversion efficiency and control flexibility. Meanwhile, there are still some obstacles and challenges in the practical application of RLVdc due to the lack of standardization. One of the most important issues in RLVdc standardization progress is the voltage level standard. In this article, a comparative study on the performance of different RLVdc system voltage levels is presented. The main purpose of the article is to provide a voltage level selection guidance for energy prosumers and equipment manufacturers interested in RLVdc systems. The voltage levels commonly used in academia and industry, from 220 to 750 V, are compared and analyzed considering their influence on compatibility, topology, power supply capability, and safety. The article contains a research surveys, market statistics, power system simulations, and calculations with simplified models. The results of the comparative study show that ±375 V is optimal for the RLVdc system in cases with large power ratings and voltage levels below 240 V are suitable for RLVdc systems with special safety requirements.

Drivers of Digitalization in the Energy Sector—The Managerial Perspective from the Catching Up Economy

This article attempts to identify the key forces driving the successful digitalization of the energy sector, ensuring improvements in the energy triangle including sustainability, stability, and economic performance. The article sheds light on the diverse energy priorities at supra-, national, and managerial levels, and the role of digitalization in achieving these objectives. Catching up economies (such as Poland), being post-socialist EU member states, in order to transform its energetic sector, must overcome a number of infrastructural and social shortcomings retained as a legacy of the socialist economy. As such, sustainability (as the core priority at EU energy agenda) may not be the leading objective at both national and company level in the energy sector transformation. This article presents the results of empirical research carried out through distribution of e-questionnaire addressed to Polish managers from the energy sector. The results were analyzed using the fsQCA method. The findings suggest that, for managers, the most important drivers of digitalization and transformation of the energy sector in Poland are its high economic performance, together with support for energy prosumers and consumers. The prerequisites for a successful digitalization are alternatively the absence of management barriers, or a combination of high economic performance and a strong focus on environmental protection. Surprisingly, according to managers surveyed, the rapid implementation of new technologies is not considered a vital condition for successful digital transformation of the energy sector, which implies either or managerial lack of knowledge in this area and/or a reluctance to introduce digital rapid technologies.

Distributed control strategy for transactive energy prosumers in real‐time markets

AbstractThe increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi‐prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. Herein, a market‐based control mechanism in real‐time markets is proposed to economically coordinate the TE among prosumers while ensuring secure system operation. Considering the dynamic characteristics of batteries and responsive demands, a model predictive control (MPC) method is used to handle the constraints between different time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between prosumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33‐bus system demonstrated the efficiency and effectiveness of the proposed method and model.

Characterizing the Danish energy prosumer: Who buys solar PV systems and why do they buy them?

Lower costs and stop-go policy around 2012 created two distinct groups of households with solar photovoltaics (PVs) in Denmark – a large group of early adopters (annually metered), and a smaller group of later adopters (hourly/real-time metered).This paper analyses these groups to characterize Danish PVs prosumer households and identify why they bought PVs.A comparison of a full population of 73,974 Danish household PVs owners (registered as of 2015) with other households shows that adopters tended to have higher incomes, be older, live in rural areas, have newer houses, and use individual heating (e.g. heat pumps). Moreover, the registered owners of PVs tended to be technically educated men.A 2018 survey of 2505 PVs owners indicates that later adopters were slightly more inspired by peers, whereas early adopters were more inspired by news articles and sales drives. Moreover, independence, financial gains, and displaying proenvironmental action are identified as motivations to adopt PVs.This paper concludes that technically educated men seem to dominate the decisionmaking process and that Danish energy prosumers seem to share (more or less) the same characteristics and motivations as PVs adopters in other contexts despite the distinct diffusion pattern in Denmark.

Energy management of Internet data centers in multiple local energy markets

Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs’ cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method.