Distributed Energy Resources Owners Research Articles

Aggregating Distributed Energy Resources: Efficiency and Market Power

Problem definition: The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, electric storage, thermal storage, and combined heat and power plants. Because of the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper the question of how to integrate these DER supplies into the electricity market, with the objective of achieving full market efficiency. Methodology/results: We study four aggregation models, where there is an aggregator who, with the knowledge of DERs’ utility functions and generations, procures electricity from DERs, and sells them in the wholesale market. In the first aggregation model, a profit-maximizing aggregator announces a differential two-part pricing policy to the DER owners. We show that this model preserves full market efficiency, that is, the social welfare achieved by this model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, the profit-seeking aggregator is forced to impose a uniform two-part pricing policy to prosumers from the same location, and we numerically show that there can be large efficiency loss. In the third (fourth) aggregation model, a uniform (semiuniform) two-part pricing policy is applied to DER owners, whereas the aggregator becomes fully regulated but is guaranteed nonnegative (positive) profit. It is shown that these models again achieve full market efficiency. Furthermore, we show that DER aggregation also leads to a reduction in the market power of conventional generators. Managerial implications: DER aggregation via profit-seeking and/or regulated aggregators has been investigated by California Independent System Operator and New York Independent System Operator, among others, and the recent Federal Energy Regulatory Commission Order No. 2222 paved the way for aggregators to bid in the wholesale market. Our four aggregation models may shed light on how DERs should be included in the wholesale electricity market. Funding: This work was supported by the King Fahd University of Petroleum and Minerals [Grant INML2106] and the National Science Foundation [Grant 1832230]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0539 .

Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach

A virtual power plant (VPP) is typically a collection of distributed energy resources (DERs) aggregated by an energy service provider (ESP). However, recruiting DER owners to participate in a VPP is challenging. Therefore, we propose a profitable and flexible VPP recruitment-participation approach that incorporates both long-term regular recruitment and short-term casual recruitment. Casual recruitment caters to ambitious DER participants, consisting of fair and bet-on modes. The latter establishes a set of pre-determined payoff conditions, the fulfillment or non-fulfillment of which confers the participants a contractual right to get compensation from the ESP. To ensure the success of the proposed recruitment approach, we address two key problems. First, we introduce a new index, unconventional arbitrage opportunity (UAO), for evaluating future profits and propose a conditional time series generative adversarial network to predict UAO with weather conditions. Second, we introduce a payoff allocation method that combines fairness and incentives to motivate casual DER participants. The incentive coefficients are optimized using an improved deep reinforcement learning algorithm. Case studies are conducted to verify the proposed recruitment-participation approach, the effectiveness of the UAO prediction model, and the optimized incentive coefficients.

Bi-Level Multi-Objective Planning Model of Solar PV - Battery Storage-Based Ders in Smart Grid Distribution System

The Bi-level Multi-Objective Planning Model of Solar PV-Battery Storage-Based DERS in Smart Grid Distribution System is a research paper that proposes a planning model for the implementation of distributed energy resources (DERs) in a smart grid distribution system. The model is designed to optimize the deployment of solar PV and battery storage systems in the grid, while taking into account various technical, economic, and environmental factors. The proposed planning model is based on a bi-level multi-objective optimization approach, which considers both the objectives of the utility and the objectives of the DER owners. The upper-level objective is to minimize the total cost of energy supply to the grid, while the lower-level objective is to maximize the revenue of the DER owners. The model is implemented using a genetic algorithm, which is used to search for the optimal solution.
 The model is also capable of considering the uncertainties associated with solar PV and battery storage systems, such as weather conditions and battery degradation. The results of the study show that the proposed planning model can effectively optimize the deployment of solar PV and battery storage systems in a smart grid distribution system. The model is also shown to be robust to various uncertainties associated with DERs, such as weather conditions and battery degradation. Overall, the proposed planning model provides a valuable tool for the implementation of distributed energy resources in a smart grid distribution system. By optimizing the deployment of DERs, the model can help to reduce the cost of energy supply, while also improving the reliability and environmental performance of the grid.
 This project describes the multi objective battery sizing and storage system for grid connected system using renewable energy solar and wind system.

Comparisons of Auction Designs Through Multiagent Learning in Peer-to-Peer Energy Trading

Distributed energy resources (DERs), such as solar panels, are growing rapidly and reshaping power systems. To promote DERs, utility companies usually adopt feed-in-tariff (FIT) to pay DER owners (aka prosumers) fixed rates for supplying energy to the grid. As an alternative to FIT, consumers and prosumers can trade energy in a peer-to-peer (P2P) fashion. In this paper, we focus on a P2P market using double auctions, in which the payoffs of energy consumers/prosumers are determined by their bids and auction mechanisms. Special features of a P2P energy auction, however, including zero marginal cost and publicly-known reserve prices, may invalidate many theories on auction design and hinder market development. We discuss the impacts of such features on four specific clearing mechanisms: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -double, Vickrey, McAfee and maximum volume matching (MVM). Furthermore, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning, in which each agent only needs to utilize their own bidding history to determine how to bid in the next round through certain regret-minimizing algorithms. Numerical results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -double and McAfee auction appear to perform better in terms of bidders’ surplus. However, if the auctioneer also requires compensation, MVM can yield the most profit for the auctioneer.

Distributed utility‐based real‐time power flow optimization in ICT‐enabled low voltage distribution grids

AbstractLow‐Voltage (LV) distribution grids are facing a rapid increase of connected Photovoltaic (PV) power plants as well as flexible consumers like Battery‐Electric Vehicle (BEV) chargers and Heat Pumps (HPs). The coordinated operation of these generation, storage and consumption units, referred to as Distributed Energy Resources (DERs), is regarded as a key requirement to maximize the benefits of renewable generation without violating, for example, voltage limits. Therefore, an operation management scheme was proposed in previous work that optimizes the power flows in LV grids in real‐time, where optimality is expressed as a maximization of the utility that DER owners experience from power consumption or injection, respectively. In this contribution, this method is extended by: (1) detailing a time‐varying utility‐model to express customer needs, (2) introducing a distributed implementation enhancing the robustness to failures, (3) developing a testbed using a real‐time digital power grid simulator and a communication network emulator, and (4) integrating a real‐time information flooding protocol. The performance is evaluated in different simulation scenarios, showing that the proposed method is able to cooperatively utilize the flexible units in order to fulfil the DER owners' needs even in the event of controller failures and constrained communication.

Using OPF-Based Operating Envelopes to Facilitate Residential DER Services

The proliferation of residential distributed energy resources (DER), such as solar PV and batteries, is creating opportunities for DER owners to provide system-level services through aggregators. However, if this aggregated response is not adequately managed by distribution companies, the simultaneous active power exports (or imports) can result in voltages and currents well beyond the limits of their networks. But the big barrier is that, often, distribution companies cannot directly manage DER or aggregators. This work proposes the use of operating envelopes—time-varying, meter-level export/import limits that a distribution company issues to aggregators—to ensure network integrity while facilitating residential DER services. The operating envelopes are calculated using a linearised, three-phase optimal power flow (OPF) technique that also exploits controllable network assets (e.g., on-load tap changers). Using a realistic Australian MV-LV network with 4,600+ residential customers, results show that network integrity can be ensured without the need for distribution companies to directly control DER. Furthermore, existing on-load tap changers create additional voltage headroom and, thus, larger operating envelopes (and potential services). Given that distribution companies do not know the ultimate exports/imports of customers providing services, a probabilistic testing of the operating envelopes confirms that they are adequate for most plausible scenarios.

Energy Trading on a Peer-to-Peer Basis between Virtual Power Plants Using Decentralized Finance Instruments

Over time, distribution systems have begun to include increased distributed energy resources (DERs) due to the advancement of auxiliary power electronics, information and communication technologies (ICT), and cost reductions. Electric vehicles (EVs) will undoubtedly join the energy community alongside DERs, and energy transfers from vehicles to grids and vice versa will become more extensive in the future. Virtual power plants (VPPs) will also play a key role in integrating these systems and participating in wholesale markets. Energy trading on a peer-to-peer (P2P) basis is a promising business model for transactive energy that aids in balancing local supply and demand. Moreover, a market scheme between VPPs can help DER owners make more profit while reducing renewable energy waste. For this purpose, an inter-VPP P2P trading scheme is proposed. The scheme utilizes cutting-edge technologies of the Avalanche blockchain platform, developed from scratch with decentralized finance (DeFi), decentralized applications (DApps), and Web3 workflows in mind. Avalanche is more scalable and has faster transaction finality than its layer-1 predecessors. It provides interoperability abilities among other common blockchain networks, facilitating inter-VPP P2P trading between different blockchain-based VPPs. The merits of DeFi contribute significantly to the workflow in this type of energy trading scenario, as the price mechanism can be determined using open market-like instruments. A detailed case study was used to examine the effectiveness of the proposed scheme and flow, and important conclusions were drawn.

Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources

In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.

Nested Bilevel Optimization for DERA Operation Strategy: A Stochastic Multiobjective IGDT Model With Hybrid Endogenous/Exogenous Scenarios

An aggregation of distributed energy resources (DERs) can bring economic and technical benefits for the DER owners and system operator. However, the operation of DERs encounters various uncertainties, which can seriously impact the benefits of DER aggregation. This article presents a new operation optimization approach for an aggregator of DERs considering the unavailability of DERs (as discrete uncertainty sources) as well as forecast uncertainties of electricity prices, solar powers, and wind powers (as continuous uncertainty sources). The proposed approach for DER aggregator (DERA) operation optimization comprises stochastic multiobjective information-gap decision theory (IGDT) to model these discrete and continuous uncertain variables. Moreover, a hybrid endogenous/exogenous scenario generation method is incorporated into the proposed approach to enhance the efficiency of the stochastic programming part by producing decision-dependent scenario trees. The proposed approach is formulated as a nested bilevel optimization model. The proposed approach is compared with other DERA operation optimization models using an out-of-sample analysis method. The comparative results illustrate the superiority of the proposed stochastic multiobjective IGDT approach over various deterministic, stochastic, and IGDT methods. In addition, the high tractability of the proposed solution method is illustrated, while its linearization error for the stochastic multiobjective IGDT problem is well below 1%.

Distribution Optimal Power Flow With Energy Sharing Via a Peer-To-Peer Transactive Market

The technology advancement and cost decline of renewable and sustainable energy increase the penetration of distributed energy resources (DERs) in distribution systems. Transactive energy helps balance the local generation and demand. Peer-to-peer (P2P) energy trading is a promising business model for transactive energy. Such a market scheme can increase the revenue of DER owners and reduce the waste of renewable energy. This article proposes an equilibrium model of a P2P transactive energy market. Every participant seeks the maximum personal interest, with the options of importing or providing energy from/to any other peer across different buses of the distribution network. The market equilibrium condition is obtained by combining the Karush–Kuhn–Tucker conditions of all problems of individual participants together. The energy transaction price is endogenously determined from the market equilibrium condition, which is cast as a mixed-integer linear program and solved by a commercial solver. The transactive energy flow is further embedded in the optimal power flow problem to ensure operating constraints of the distribution network. We propose a remedy to recover a near optimal solution when the second-order cone relaxation is inexact. Finally, a case study demonstrates that the proposed P2P market benefits all participants.

Optimal operation of small, numerous, and disparate DERs via aggregation in transactive distribution systems with universal metering

Transactive energy distribution systems (TEDS) have unleashed new economic opportunities in the distribution sector via a new local distribution operator (LDO) that enables transactions between peers and the LDO (P2LDO) and peer-to-peer transactions. Aiming to unleash full benefits from existing distributed energy resources (DERs), this paper introduces an optimization algorithm for the operation of small, numerous, and disparate DER aggregations. The proposed algorithm seeks to maximize aggregator profits obtained via P2LDO and peer-to-peer transactions for energy and demand response, while being cognizant of capacity obligations acquired during transactive energy distribution system planning phases. In addition, the concept of universal metering is introduced to upgrade the economic opportunity of DERs. Results obtained via case studies show that the proposed approach can help DER owners to increase their revenue. An aggregated case study for 300 m shows that the overall revenue can be increased by more than 100% when operating DERs in an aggregated fashion.

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption.

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

Who knows what: information barriers to efficient DER roll-out

While academic research on Distributed Energy Resources (DERs) has been mostly focused on first-best systems, we hypothesize that in reality multiple information barriers to efficient DER roll-out exist. We thus study the prevalence and importance of information issues arising in the context of deployment of DERs by reviewing the existing engineering and economic literature on distributed resources, analyzing DER-related regulatory proceedings, and surveying the relevant electricity sector stakeholders for their perception of information relevance and accessibility. Our findings suggest that there are substantial information problems, especially faced by DER owners and developers as well as non-profit organizations such as environmental groups. Some of the problems hinder the efficiency of DER roll-out, warranting new regulation, thereby indicating the role of policymakers for the future DER developments.

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator’s hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

Aggregating Distributed Energy Resources: Efficiency and Market Power

The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, small natural gas-fueled generators, combined heat and power plants, etc. Due to the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper two efficient aggregation models. In the first aggregation model, a profit-maximizing aggregator procures electricity from DERs, and sells them in the wholesale market. We show that this model preserves full market efficiency, i.e., the social welfare achieved by the aggregation model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, a uniform two-part pricing policy is applied to DER owners, while the aggregator becomes fully regulated but is guaranteed positive profit. It is shown that this second model again achieves full market efficiency. Furthermore, we show that DER aggregation can also leads to a reduction on the market power of conventional generators. All arguments are supplemented with illustrative examples.

Increment‐correction method for online distributed optimisation in distribution networks with extra‐high DER integration

This study focuses on future distribution networks featuring the integration of extra-high distributed energy resources (DERs). In these distribution networks, the extra-high DER penetration and the randomness and volatility of DER outputs increase the likelihood of voltage problems. Hence, it is necessary to design efficient solutions for DER output setpoints to regulate the voltages of distribution networks. However, the existing online distributed algorithms were designed based on different linearised technologies for power flow equations, which will cause imprecise results under extra-high DER integration. In this study, the authors propose an increment-correction method to improve the linearised power flow model. To obtain an efficient distributed manner, they introduced an indirect function into the end-customer (the DER owner)'s objective function, stimulating them to provide voltage-regulating service. In particular, two price incentive signals contained in the indirect function were transmitted from the distribution system operator to drive the DER owner to regulate output increments, which is suitable for cases where the DER belongs to different end-customers. The proposed method can not only protect the private information of end-customers but also enhance the robustness of the distribution network for the DER integration level. Numerical test results demonstrate the merits of the proposed method.

Compensation for Power Loss by a Proof-of-Stake Consortium Blockchain Microgrid

Distributed generation in the microgrid becomes increasingly significant, as it eliminates the power losses from long-distance electricity transmission lines. Distributed energy resources owners who can both produce and consume energy are defined as prosumers. To encourage the peer-to-peer (P2P) energy trading between prosumers, blockchain as a thriving technology is utilized in the P2P network due to its transparency, security, and rapidity in executing transactions. Due to its decentralized quality, any intermediaries are eliminated so that transactions happen directly among traders. This article introduces a consortium blockchain trading model to support P2P energy trading, using a proof-of-stake protocol. The pre-selected miners are responsible for compensating the power losses in distribution lines by energy transactions. The specific process of the blockchain establishment, as well as the smart contract creation, are demonstrated. In addition, a type of crypto-currency named “elecoin” is created in the P2P market, which is published by the mining mechanism of the blockchain. Finally, a case study is introduced to realise the functions of the proposed blockchain model. Simulation results show the feasibility and effectiveness of the proposed approach.

Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study

Transactive energy is a novel approach for energy management and trading, which can be used in microgrids to facilitate the integration of distributed energy resources (DERs) in existing networks. The key feature in transactive energy is using market-based solutions for energy management. Hence, an appropriate transactive energy market (TEM) framework should be designed to enable and incentivize DER owners to participate in different markets. The efficient implementation of TEM for microgrid energy management encompasses the application of a variety of design principles. In this rapidly developing area, this paper presents a complete proposal of the TEM as a framework for the design, implementation, and deployment of transactive energy solutions for energy management in microgrids. In particular, we outline the requirements to design an effective market mechanism for the TEM. The applicability of this perspective is demonstrated through the introduction of the Monash Microgrid as a real-world implementation of a TEM solution, where a complete hardware and software foundation is presented as a platform to deploy a market-based solution for microgrid energy management. This is further illustrated through an example scenario, where the application of TEM is discussed to demonstrate the impact of considered design choices on achieving desired objectives.

Flexible interconnection analysis with EPRI DER integration testbed

Due to increasing distributed energy resources (DER) penetration, the DER hosting capacity of the existing distribution grid can be limited, and costly grid upgrades may be required due to potential but infrequent worst-case scenarios. DER management system (DERMS) can enable additional DER capacity to be interconnected to the existing distribution grid as a managed resource that can be curtailed to avoid violations under the infrequent worst case as an alternative to costly grid upgrades. With DER interconnected under flexible interconnection arrangements, DERMS can actively manage the grid constraints to avoid violations. This active management of DER under flexible interconnection would boost DER capacity, increase distribution system utilisation, and provide economic benefits to the DER owner through reduced interconnection costs. In this study, flexible interconnection capacity solutions (FICSs) are analysed for cases with limited hosting capacity. Testing is performed with a commercial DERMS solution provided by Smarter Grid Solutions. The study involves analysis of different principles of access for understanding technical benefits of constraint management, economic value estimation, utilisation of assets and deployment challenges involved in integrating these solutions. The study points out the key learnings and next steps for integrating FICS into the planning and operation of the grid.

Quantifying Market Efficiency Impacts of Aggregated Distributed Energy Resources

We focus on the aggregation of distributed energy resources (DERs) through a profit-maximizing intermediary that enables participation of DERs in wholesale electricity markets. Particularly, we study the market efficiency brought in by the large-scale deployment of DERs and explore to what extent such benefits are offset by the profit-maximizing nature of the aggregator. We deploy a game-theoretic framework to study the strategic interactions between an aggregator and DER owners. The proposed model takes into account the stochastic nature of the DER supply. We explicitly characterize the equilibrium of the game and provide illustrative examples to quantify the efficiency loss due to the strategic incentives of the aggregator. Our numerical experiments illustrate the impact of uncertainty and amount of DER integration on the overall market efficiency.