Search Results (10)

California is currently navigating the confluence of two acute systemic challenges: a chronic housing affordability deficit and increasing grid instability driven by climate-induced volatility and the aggressive transition to variable renewable energy. This review posits that the answer lies in a novel technology convergence: the strategic integration of Accessory Dwelling Units (ADUs) with residential Battery Energy Storage Systems (BESSs) utilizing the “Photovoltaic-Energy Storage-Direct Current-Flexibility” (PEDF) architecture. We identify this ADU + BESS + PEDF nexus as a critical innovation that transforms the dwelling unit from a passive consumption endpoint into an active highly efficient DC-coupled “prosumer” node capable of providing critical grid services. Unlike traditional AC-coupled systems, the PEDF framework minimizes conversion losses and maximizes grid-interactive flexibility, establishing the ADU as a decentralized asset for grid stabilization. To validate this paradigm, I employ a stochastic financial simulation using the RShiny framework to assess the economic viability of prefabrication-based deployment strategies under Senate Bill 9 (SB 9) provisions for three investment scenarios: Acquisition-to-Rent, Acquisition–Development–Resale, and Long-Term-Asset-Retention. Benchmarked against a baseline of traditional in situ constructions globally, our results indicate that modular prefabrication reduces project timelines by 30–50% and cradle-to-site embodied carbon by up to 47%. Furthermore, financial modelling—benchmarked at a 7.5% nominal discount rate without discretionary state incentives—confirms that “Acquisition–Development–Resale” strategies yield Internal Rates of Return (IRR) exceeding 20%, while “Long-Term-Asset-Retention” achieves stabilized positive cash flow, validating the economic competitiveness of sustainable densification. Despite identifying implementation barriers—specifically the “split-incentive” dilemma in rental markets and emerging data sovereignty constraints—this review concludes that the BESS-powered PEDF-architecture ADU represents the fundamental atomic unit of a resilient, low-carbon urban dwelling infrastructure, necessitating aligned policy support to achieve scalable deployment. Full article

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

This paper presents a fully-decentralized peer-to-peer (P2P) electricity and gas market for retailers and prosumers with coupled energy units, considering the uncertainties of wholesale electricity market price and prosumers’ demand. The goal is to improve the overall economy of the proposed market while increasing its flexibility. In this market, the retailers are equipped with self-generation and energy storage units and can bilaterally negotiate for electricity and gas transactions with prosumers to maximize their profit. Furthermore, they can sell power to the upstream market in addition to prosumers. The prosumers have access to several retailers to supply their required electricity and gas and can freely provide their energy needs from every retailer, contributing to dynamicity in the proposed market. Given that they have an energy hub consisting of boiler units, combined heat and electricity (CHP) units, and electric pumps, they can switch their energy supply source from electricity to gas and vice versa. A robust possibilistic programming approach is applied to address the uncertainties. A fully-decentralized approach called the alternating direction method of multipliers (ADMM) is utilized to solve the presented decentralized robust problem. The proposed decentralized algorithm finds an optimum solution by establishing a smart balance between the average expected value, optimality robustness, and feasibility robustness. The feasibility and competitiveness of the proposed approach are evaluated through numerical studies on a distribution system with two retailers and three prosumers. The data analysis of the simulation results verifies the effectiveness of the proposed decentralized robust framework as well as the proposed decentralized solution. According to the maximum deviation, the expected optimal value in the robust case, the retailer’s profit has decreased by 12.1 percent, and the prosumers’ cost has increased by 27.4 percent due to the feasibility penalty term. Full article

This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources. Full article

A virtual power plant (VPP) can aggregate distributed renewable energy and flexible load to participate in the electricity market as a virtual controllable assembly. This pattern can effectively avoid the bidding risk of users, and produce cooperation benefits such as reducing transaction costs. Reasonable profit allocation is the key factor to determine the formation and survival of a VPP, which means a reasonable allocation for the VPP’s market income among participating members. In view of that, this paper proposes a framework of profit allocation in VPPs based on cooperative game theory. Aiming at the competitive environment with multiple VPPs in the electricity market, a VPP’s profit allocation model based on bidding optimization is built, which considers multiple objectives such as fairness of profit allocation, stability of cooperation alliance, and attraction of participating members. Furthermore, a multi-objective evolutionary optimization algorithm based on reference points is introduced to solve the model. Then, a VPP composed of prosumers is taken as an example to carry out the emulation. The results show that all participating members can get satisfactory profit allocation. Its cost-saving ratio ranges from 7.82% to 18.66%, and it confirms that the proposed profit allocation method can encourage prosumers of small size to participate in the VPP cooperation effectively. Full article

The electrical power system is evolving in a way that requires new measures for ensuring its secure and reliable operation. Demand-side aggregation represents one of the more interesting ways to provide ancillary services by the coordinated management of a multitude of different distributed resources. In this framework, aggregators play the main role in ensuring the effectiveness of the coordinated action of the distributed resources, usually becoming mediators in the relation between distribution system operators and smart prosumers. The research project DEMAND recently introduced a new concept in demand-side aggregation by proposing a scheme without a central aggregator where prosumers can share and combine their flexibility with a collaboration–competition mechanism in a platform called Virtual Aggregation Environment (VAE). This paper, after a brief introduction to the DEMAND project, presents the algorithm for the day-ahead estimation of prosumers’ flexibility and the cooperative–competitive algorithm for the bottom-up aggregation. The first algorithm evaluates various couples of power variation and desired remuneration to be sent to the VAE for further elaborations and, for showing its potentiality, is applied to two different case studies: a passive user with only controllable loads and prosumers with controllable loads, photovoltaics and a storage system. The aggregation algorithm is instead discussed in detail, and its performance is evaluated for different population sizes. Full article

Global warming contributes to the worldwide goal of a sustainable carbon-neutral society. Currently, hydroelectric, wind and solar power plants are the most competitive renewable technologies. They are limited to the primary resource availability, but while hydroelectric power plants (HPPs) can have storage capacity but have several geographical limitations, wind and solar power plants have variable renewable energy (VRE) with stochastic profiles, requiring a substantially higher investment when equipped with battery energy storage systems. One of the most affordable solutions to compensate the stochastic behaviour of VRE is the active participation of consumers with demand response capability. Therefore, the role of citizen energy communities (CECs) can be important towards a carbon-neutral society. This work presents the economic and environmental advantages of CECs, by aggregating consumers, prosumers and VRE at the distribution level, considering microgrid trades, but also establishing bilateral agreements with large-scale VRE and HPPs, and participating in electricity markets. Results from the case-study prove the advantages of CECs and self-consumption. Currently, CECs have potential to be carbon-neutral in relation to electricity consumption and reduce consumers’ costs with its variable term until 77%. In the future, electrification may allow CECs to be fully carbon-neutral, if they increase their flexibility portfolio. Full article

The electric power systems together with the entire energy sector are rapidly evolving towards a low-carbon, secure, and competitive economy facing revolutionary transformations from technical structure to economic value chain. Pathways to achieve sustainability led to the development of new technologies, accommodation of larger shares of unpredictable and stochastic electricity transfer from sources to end-users without loss of reliability, new business models and services, data management, and so on. The new technologies and incentives for local energy communities along with large development of microgrids are main forces driving the evolution of the low voltage energy sector changing the context and paradigm of rigid contractual binding between utilities and end-user customers (now progressing to flexible prosumers with generation and storage capabilities). The flexibility and operation of a prosumer can be enhanced by a non-intrusive time-frequency analysis of distorted power quality waveforms for both generation and demand at the point of common connection. Therefore, it becomes of importance to discriminate among successive quasi-steady-state operation of a given local system using only the aggregated waveforms information available in the PCC. This paper focuses on the Hilbert–Huang method with modifications such as empirical mode decomposition improved with masking signals based on the Fast Fourier Transform, Hilbert spectral analysis, and a post-processing method for separating components and their amplitudes and frequencies within distorted power signals for a low-voltage prosumer operation. The method is used for a time-frequency-magnitude representation with promising localization capabilities enabling efficient operation for prosumers. Full article

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128 bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design. Full article

The principal aim of this study is to devise a combined market operator and a distribution network operator structure for multiple home-microgrids (MH-MGs) connected to an upstream grid. Here, there are three distinct types of players with opposite intentions that can participate as a consumer and/or prosumer (as a buyer or seller) in the market. All players that are price makers can compete with each other to obtain much more possible profitability while consumers aim to minimize the market-clearing price. For modeling the interactions among partakers and implementing this comprehensive structure, a multi-objective function problem is solved by using a static, non-cooperative game theory. The propounded structure is a hierarchical bi-level controller, and its accomplishment in the optimal control of MH-MGs with distributed energy resources has been evaluated. The outcome of this algorithm provides the best and most suitable power allocation among different players in the market while satisfying each player’s goals. Furthermore, the amount of profit gained by each player is ascertained. Simulation results demonstrate 169% increase in the total payoff compared to the imperialist competition algorithm. This percentage proves the effectiveness, extensibility and flexibility of the presented approach in encouraging participants to join the market and boost their profits. Full article