Komeil Moghaddasi, Raja Jurdak, Sara Khalifa, Yuchen Zhang · 7 authors
The rapid growth of distributed energy resources (DER) such as rooftop photovoltaics (PV), battery storage, electric vehicles (EV), and flexible loads, is shifting power system coordination from centralised control centres to millions of prosumers and local controllers at the distribution level. This transition has led to many new coordination approaches across control, market, and learning-based models, which are often described as decentralised. However, this term is applied inconsistently: it may refer to decomposed optimisation, edge computing, peer-to-peer (P2P) trading, or distributed ledger technology, obscuring what is actually being decentralised, authority, computation, information, or topology. Existing surveys typically address one such concept in isolation, for example, microgrid control structures, energy management system (EMS) topologies, or market designs, without providing a unified, multi-dimensional view across the full coordination landscape. In this survey, we propose a six-tier graduated decentralisation scale for distribution level coordination architectures, accompanied by a set of classification criteria that we apply to systematically map and compare recent architectures. We discuss how topology, decision-making, autonomy, intelligence, information flow, and coordination mechanisms evolve as architectures move from centralised to more decentralised operation. We further identify concrete research gaps, and outline future directions for deployment grade, multi-actor grid coordination.
Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning.
The Local Energy Market (LEM) is a key element in the energy sector's transition toward a decentralized system, enabling the integration of a growing number of small generation sources and energy storage facilities located at end-user locations.By utilizing digital energy trading platforms provided by LEMs, small consumers, producers, and prosumers actively participate in system balancing, which, among others, allows them to increase profits and energy independence.The efficiency of energy exchange in LEM is achieved by means of optimization methods that make use of sensitive participant data, such as energy consumption profiles.Therefore, ensuring privacy while simultaneously ensuring trust in the achieved optimal quantitative and qualitative results is crucial.The classic technology used in decentralized systems, i.e., blockchain, does not provide adequate scalability when transactions result from solving optimization problems.In this article, we analyze the possibilities of verifying optimization results by the use of cryptographic zero-knowledge proofs (ZKP).We explain how ZKP can support privacy and enable verification of computations without the need of repeating them for every participant.We also refer to existing ZKP implementations on LEM, while highlighting the barriers of high computational costs that prevent direct implementation of complex optimization algorithms within ZKP protocols.'To overcome these barriers, we present an approach integrating ZKP with optimality certificates, which has significant potential to increase the efficiency of
Inter-provincial electricity transactions within China’s unified power market are complicated by spatial heterogeneity, asynchronous dispatch timelines, and strategic deviations in bilateral commitments. Existing mechanisms often struggle with ex-post contestability, temporal inconsistencies, and poor alignment between real-time system conditions and deviation pricing, undermining the market’s fairness and reliability. To address these challenges, this paper proposes a novel Tri-Ledger Coordinated Settlement (TCS) framework with built-in temporal consistency. The tri-ledger design consists of (1) a Contract Ledger capturing day-ahead bilateral schedules, (2) a Dispatch Ledger reflecting system-level nodal redispatch outcomes, and (3) a Deviation Ledger reconciling discrepancies across provinces through an enforceable and tamper-resistant protocol. Central to this framework is a Distributionally Robust Deviation Pricing (DRDP) model, which penalizes deviation behaviors not based on deterministic thresholds but through ambiguity-aware dual pricing anchored in Wasserstein-ball uncertainty sets. This allows the pricing system to anticipate manipulative strategies while offering probabilistic fairness to genuine imbalances caused by renewables or congestion. Furthermore, a Non-Contestable Decoupled Execution Mechanism (NCDEM) is developed to isolate provincial profit zones during redispatch operations, ensuring that a province cannot benefit by manipulating its declared bilateral trades or influencing others’ deviation compensations. The proposed approach guarantees strategy-proofness under minimal information assumptions and supports distributed execution by provincial grid companies without centralized re-optimization. The effectiveness of the framework is demonstrated on a stylized multi-province testbed derived from China’s Eastern and Central grid clusters. Numerical experiments show that the DRDP-based settlement leads to over 18.4% improvement in fairness-adjusted social welfare and reduces strategic deviation incentives by up to 73% compared to deterministic baseline models. Sensitivity analyses validate robustness under multiple load and RES penetration scenarios. The proposed TCS framework offers policy-relevant insights for implementing transparent and resilient provincial electricity market settlements under China’s “dual-track” trading architecture.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
In this thesis, we categorize the challenges that Distribution System Operators (DSO) are facing into two separate sets of articles. After the introduction, the initial set of articles (chapters 2, 3, and 4) focuses on network operation, addressing challenges, and suggesting creative solutions to enhance the resilience and effectiveness of decentralized energy systems. The subsequent set of articles (chapters 5,6,7 and 8) redirects attention to the exploration of energy communities, unveiling the potential of localized, participatory energy ecosystems. Chapter 2 can be summarized as follows. In an electrical system where decentralized and embedded productions are becoming increasingly important, it is essential to ensure a good understanding of their behavior at their operating limits. One of the most important operating limits is when the system frequency approaches 50.2 Hz. At this frequency, following the old requirements, many existing European PV inverters have to be disconnected. In such situations, we demonstrate that the variance of the frequency measurement taken at every PV inverter plays a key role. It has been demonstrated that this variance is a good thing from the system's point of view as it allows for a gradual disconnection, leading to a controlled variation of the frequency. To address the challenges due to decentralized energy generation and emerging loads like electric vehicles, DSOs implement Active Network Management (ANM) as a short-term strategy to manage efficiently power injection and consumption, avoiding congestion without the need for heavy infrastructure investment. ANM requires knowledge of the system state, necessitating the placement of measurement devices throughout the network to ensure accurate estimates. In that context, chapter 3 introduces a new method for placing measurement devices in distribution networks. In contrast to the previous research works which rely on objectives for the placement such as state estimation accuracy, the proposed method incorporates ANM considerations in the process of determining the optimal locations, aiming to enhance ANM quality. Simulation results on a test distribution network demonstrate the superiority of this approach, leading to reduced curtailment of generators and improved overall performance. Grid monitoring strategies, like the one presented in Chapter 3 is the process of collecting data from sensors across a distribution grid and sending it to a central system (SCADA) to identify and diagnose problems, improve reliability, and save energy and money. The increasing complexity of power flows and the need to manage them using ANM strategies requires accurate data and strong defenses against cyber attacks. A proof-of-concept software called "MonitORES" was developed using Hyperledger Fabric to demonstrate how a distributed ledger technology (DLT) such as blockchain can be used to monitor and control generation units within ANM schemes, with improved resilience against cyberattacks. It is this work that is presented in the chapter 4. Chapter 5 opens the second set of articles aiming to explore renewable energy communities (REC). The main goal of the E-Cloud, one of the first projects of energy communities in Wallonia, as with every microgrid, is to maximize the consumption of energy produced locally. To reach this goal, based on consumption profiles of customers willing to participate in the E-cloud and given some local restrictions (e.g. wind turbines cannot be put everywhere), an optimal mix of green generation sources (in kW) and local storage (in kWh) needs to be computed. Then according to this computation, the required generating units and storage devices are installed. A repartition mechanism grants the customer a share of the generated electricity and storage capacity. These shares are either computed offline or dynamically adapted online. The project aimed to test two models: either the DSO or a producer owns and operates the storage device. Two information flows (real-time for the operation of the storage facility and ex-post for its settlement) are needed to ensure correct information exchange with the wholesale market. These information flows are completed thanks to a forecast that provides members of the E-Cloud the full capability to anticipate and obtain the maximum benefits of the local generation. The expected benefits for the customer are a reduction of their electricity bill by a minimum of 10\%. Societal benefits should also arise: 1) easing the technical integration of renewables generation embedded in the distribution network, and 2) avoiding extra investment in the DSO network. The next chapter proposes that the success of local REC, now foreseen by the European Union directives but also growing worldwide, will rely on the appetite of consumers and investors. This is not obvious when the target local area is a residential community where people have varying expectations. Based on Bayesian game theory (also called a game of incomplete information), the purpose of this paper is to define an approach for determining, from the point of view of the renewable energy investor, the level of production capacity and energy price that needs to be offered to the consumers. Chapter 7 explores how the blockchain approach can be employed to foster this REC market. The goal is to determine the design that should allow a DSO to accept peer-to-peer energy exchanges based on a distributed ledger supported by blockchain technology. To this end, an evaluation is conducted integrating several designs based on criteria such as acceptance of the wholesale/retail market, the resilience of the consensus to approve a block, the accuracy, traceability, privacy, and security of the proposed schemes. Chapter 8 poses that, despite its success and large use in other crypto-currencies, Proof of Work's disadvantages are high latency, a low transaction rate, and a high energy expenditure, making it a less-than-perfect choice for many applications. In addition, the validation of transactions is not carried out with a definite temporality. However, for certain use cases such as auctions or the exchange of energy in the REC context, there is a need for this temporality. The purpose of this article is to propose a new type of consensus that is faster, less energy-consuming and that can be synchronized with a time reference. The core of the reflection is the use of the Condorcet voting mechanism to determine the miner. The last chapter sets the main conclusion of this research. Two appendixes show other works conducted with fellow researchers during this PhD research journey.
Julia Groza, Seyyed Ali Sadat, Koami Soulemane Hayibo, Joshua M. Pearce
To assist electric utilities to overcome limitations of centralized billing and encourage distributed production of solar photovoltaic (PV) electricity, this study designs and assesses a novel open-source autonomous virtual utility to monitor users and enable peer-to-peer trading. This study provides system design and software implementation of the concept using blockchain technology written in Solidity and Truffle. A set of smart contracts adds users to a system and monitors their demand, PV generation, and facilitates transactions between users on an hourly basis when one user has PV-generated excess electricity, and another has demand. Unit tests for each of the contracts’ methods are developed in Solidity, and data on gas usage and costs is collected. Once the contracts have been written and evaluated, a JavaScript simulation is developed to use the contracts on real load and PV generation data for one year on an hourly basis. The results of two case studies are quantified: 1) true peers, where all houses are prosumers with rooftop PV, and 2) intermittent transition case, where PV deployment and demand are more varied. The results found that with ten users in the system, the true peers case study resulted in an uneconomic number of exchanges, but the intermittent transition case study resulted in more than a factor of twenty increases in exchanges and net cost savings. The savings more than doubles for both cases when time of use pricing is in effect. The system utility increases with more variability of PV production across participating users and is recommended for utilities targeting increases in distributed generation during the energy transition.
Tian Wang, Ning Zhou, Yongfeng Yang, Jingnan Li · 5 authors
With the large-scale integration of new energy sources like distributed photovoltaics, distribution grids are facing challenges related to reactive power flow, voltage distribution, and power loss. This paper introduces a distribution grid load balancing method based on regional precision control to accommodate the integration of new energy, ensuring the safety, stability, and economic operation of the distribution grid. A comprehensive model of the distribution grid, including new energy sources, has been established. By combining the asset ledger and operational data of medium voltage graphical model equipment, a panoramic simulation environment is created.Utilizing the improved Newton-Raphson power flow calculation method and considering the safety of the distribution system and operational constraints of the energy storage system, the dynamic load and operational loss of the distribution grid are analyzed. Through precise analysis, the optimal operational mode of the grid lines and the opening and closing positions of switches are determined, achieving relatively balanced load and minimal operational loss. Taking into account the output of new energy and equipment load characteristics, staggered and orderly electricity usage strategies are formulated to reduce peak electricity load and alleviate operational pressure on the grid.Simulation results indicate that this method thoroughly considers the randomness and uncertainty of distributed photovoltaics. Through precise control strategies, it effectively reduces the operational loss of the distribution grid, ensures power balance and voltage stability, and meets the requirements for the safety and economic operation of the distribution grid.
Traditional power systems always rely on the fossil-fuel based power, the power is determined and dispatched in the centralized decision-making process. However, recent years have seen the increasing proliferation of distributed energy resources (DERs), communication, computing, and information devices in power systems, becoming next-generation autonomous power systems. On the one hand, the high penetration of DERs can provide a variety of benefits to next-generation autonomous power systems. For example, DERs can respond rapidly to near-term generation or reliability-related requirements, further improving their ability to enhance power system reliability and reduce costs. On the other hand, DERs have led to significant uncertainty and intermittency in power system controls and operations, especially for power system economic dispatch and voltage regulation problems. It becomes increasingly urgent to explore how to utilize DERs to improve power system efficiency, reliability, and resilience while mitigating the negative impacts of DERs on power systems. Traditional power system decision-making is the centrally-managed formulation and solution of system-wide optimizations. It always entails large amounts of computation time and information coming from customers, leading to customer privacy and scalability problems. Particularly, the capacity of each DER is always small, but the number of DERs in power systems is massive. Given such distribution characteristics of DERs, it might be impractical to apply traditional power system decision-making to autonomous power systems. To figure out this dilemma caused by DERs, it calls for new and innovative decision-making strategies to adapt to new characteristics of autonomous power systems: (1) The capacity of DERs is usually small, but the number of DERs is very massive. In addition, DERs are distributed across power systems. Coordinating massive DERs at different network locations is a big scalability challenge. (2) The uncertain and intermittent nature of DERs makes the operations of autonomous power systems more complicated, leading to different environmental change rates. Different environmental change rates might require different decision-making strategies. Offline decision-making strategies are suitable for slow environmental change rates since there is enough time for the algorithm convergence. In contrast, fast environmental change rates require online decision-making strategies to adjust the decision variables in real time. (3) The increasing deployment of communication, computing, and information devices, along with increasing data, will bring many opportunities and changes to autonomous power system decision-making. It has attracted increasing attention worldwide utilizing these devices and data to make better decisions for autonomous power systems. To this end, this work aims to propose scalable offline and online decision-making for next-generation power systems, utilizing DERs to enhance power system efficiency, reliability, and resilience. In particular, we focus on developing and designing offline and online decision-making to resolve a series of power system problems, including energy management, voltage regulation, and power flow problems. Chapters 2-3 mainly focus on the scalable offline power system decision-making, and Chapters 4-5 mainly focus on the scalable online power system decision-making. Chapter 2 develops a consensus-based transactive energy design managed by an Independent Distribution System Operator (IDSO) for an unbalanced distribution network. The network is populated by welfare-maximizing customers with price-sensitive and fixed loads who make multiple successive power decisions during each Operating Period (OP). The IDSO and customers engage in a negotiation process in advance of each OP to determine retail prices for OP that align customer power decisions with network constraints in a manner that preserves customer privacy. Convergence and optimality properties of this proposed design are established for an analytically formulated illustration: an unbalanced radial distribution network, populated by households, that is electrically connected to a relatively large regional transmission organization/independent system operator-managed transmission network. Chapter 3 aims to mitigate the voltage deviations and reduce the cost of supplying reactive power in distribution networks by optimally setting the reactive power of DERs. It proposes two types of Volt/VAr Control (VVC) strategies, including the hierarchical and decentralized VVC, based on a novel fast alternating direction method of multipliers (ADMM). For the fast ADMM-based hierarchical VVC strategy, it requires a central agent to iteratively communicate with local bus agents, but both the central agent and local bus agents can update variables in a closed form without solving sub-optimization problems. In contrast, the fast ADMM-based decentralized VVC strategy only requires the minimal information exchange between neighboring buses, but solving sub-optimization problems is necessary for local bus agents. Chapter 4 proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of DERs. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local voltage measurements, without requiring communications. Stability, convergence and optimality properties of this local voltage control are analytically established. Numerical test cases are performed to validate and demonstrate the effectiveness and superiority of ASALVC. Chapter 5 proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model, and updates the model parameters via online feedback by leveraging the instantaneous measurements of voltages and load consumption. Exploiting the connection structure of unbalanced radial distribution networks, we also provide a unified matrix-vector compact form of the model. Chapter 6 proposes an online voltage control strategy of DERs, based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal VVC problem is formulated as an optimization program with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based VVC solution algorithm is developed to solve this problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm. Moreover, taking advantage of the instantaneous feedback of voltage measurements, the online implementation of the PNM-based voltage control is further designed to deal with fast system variations.
In this paper, a novel microgrid (MG) restoration framework is proposed based on Blockchain Technology (BCT). The proposed method consists of a two-stage restoration process. In the first stage, stable Blockchain (BC) links are formulated with the grid-forming Distributed Energy Resources (DERs). In the second stage, load assignment is carried out based on the priority level of the load. The miners run through the consensus mechanism to accommodate the priority-based loads to their corresponding BC links. The consensus mechanism provides the value of an index, known as Combined Stability Measurement (CSM). The BC link, with the higher CSM value, is declared as the winner of the consensus mechanism. Subsequently, the targeted priority load is assigned to that winner BC link. The proposed BCT-based restoration framework is tested with the modified IEEE-33 and IEEE-69 bus test systems using the Ethereum blockchain platform.
The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptocurrency mining data centers has witnessed significant growth worldwide. If well-coordinated, these data centers could be tailor-designed to aggressively absorb the increasing uncertainties of energy supply and, in turn, provide valuable grid-level services in the electricity market. In this paper, we study the impact of integrating new cryptocurrency mining loads into Texas power grid and the potential profit of utilizing demand flexibility from cryptocurrency mining facilities in the electricity market. We investigate different demand response programs available for data centers and quantify the annual profit of cryptocurrency mining units participating in these programs. We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas. Our preliminary results show that depending on the size and location of these new loads, we observe different impacts on the ERCOT electricity market, where they could increase the electricity prices and incur more fluctuations in a highly non-uniform manner.
Yaçine Merrad, Mohamed Hadi Habaebi, Siti Fauziah Toha, Md. Rafiqul Islam · 6 authors
Recent advances in control, communication, and management systems, as well as the widespread use of renewable energy sources in homes, have led to the evolution of traditional power grids into smart grids, where passive consumers have become so-called prosumers that feed energy into the grid. On the other hand, the integration of blockchain into the smart grid has enabled the emergence of decentralized peer-to-peer (P2P) energy trading, where prosumers trade their energy as tokenized assets. Even though this new paradigm benefits both distribution grid operators and end users in many ways. Nevertheless, there is a conflict of interest between the two parties, as on the one hand, prosumers want to maximize their profit, while on the other hand, distribution system operators (DSOs) seek an optimal power flow (OPF) operating point. Due to the complexity of formulating and solving OPF problems in the presence of renewable energy sources, researchers have focused on mathematical modeling and effective solution algorithms for such optimization problems. However, the control of power generation according to a defined OPF solution is still based on centralized control and management units owned by the DSO. In this paper, we propose a novel, fully decentralized architecture for an OPF-based demand response management system that uses smart contracts to force generators to comply without the need for a central authority or hardware.
The proliferation of renewable generation brings challenges to the power supply-demand balance. To relieve the power fluctuations caused by photovoltaic (PV) generation variability, a multi-timescale allocation algorithm that considers the allocation of available energy and power is proposed. Contracted demand response energy (CDRE) refers to the regulation energy, which is specified in contracts in advance, that can be used to relieve power fluctuations. First, the capacity of CDRE and available regulation power (RP) provided by load aggregators (LAs) that aggregate different demand response resources (DRRs) are estimated. In hour-timescale, CDRE is allocated to maximize the control economy of all participants, where a sample average approximation-based Stackelberg game is proposed to optimize the behavior of each participant based on considering PV generation uncertainty. In minute-timescale, RP is allocated to smooth the power fluctuations and minimize the power deviations based on the allocated CDRE results. Simulation on a modified IEEE-24 bus system verifies the effectiveness of the proposed algorithm in terms of reducing the power supply-demand imbalance with maximum revenue.
T. Yuvaraj, K.R. Devabalaji, S. Srinivasan, Natarajan Prabaharan · 7 authors
The electric power distribution plays a crucial part in the power systems to maintain the power quality and stability of the system for loss mitigation and voltage regulation. The reactive power compensators like a capacitor, DG, and DSTATCOM play a vital role in power quality and stability improvement by providing proper reactive power in the distribution system. This article provides a critical review and comparative analysis of various compensating devices allocation to achieve power loss mitigation and voltage regulation in the radial distribution system (RDS). The Bat Algorithm (BA) and Voltage Stability Index (VSI) are utilized for determining the optimal size and site of the compensating devices in the RDS. Further, the application of Blockchain technology is utilized for voltage regulation of energy trading RDS. To show the effectiveness of the present study, an IEEE 33 test system is considered. All three compensators are implemented in IEEE 33 test system and compared with existing approaches. The present study is very much helpful to the Distribution Network Operators for selecting the suitable compensator in real-time applications.
Mingyu Yan, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah Abusorrah · 9 authors
This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints.
Zhichao Ren, Wei Wang, Bo Chen, Xin Li · 7 authors
With the increase of the penetration rate of distributed generation on the distribution network side, the access of a large number of prosumers makes the trading information massive, and the demand of prosumers for more flexible power trading mechanism is also strengthened. Therefore, a weak-centralized power trading mode based on blockchain is proposed in this paper. Trading information is automatically stored in the blockchain in the form of smart contracts. The centralized organization only manages congestion and does not participate in the process of trading matching and settlement. In the distributed security verification, the successive over relaxation (SOR) iterative method is improved in this paper, which improves the iterative efficiency and convergence stability of the distributed algorithm. Finally, a case consisting of six nodes is presented to verify the feasibility of the method.
The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem's effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.
Baraa Mohandes, Mohamed Shawky El Moursi, Nikos Hatziargyriou, Sameh El Khatib
This article proposes a DR program characterized by a novel compensation scheme. The proposed scheme recognizes the different characteristics of curtailment, such as the total length of curtailments within a window of time, or the number of separate curtailment events (i.e., curtailment startup), and compensates the end-user accordingly. The proposed compensation scheme features a piece-wise reward function comprised of two intervals. DR participants receive a onetime reward upfront when they enroll in the DR program and accept a set of predefined curtailment aspects. Curtailment aspects in excess of the agreed quantities are rewarded at a linear rate. This design is tailored to appeal to residential DR participants, and aims to secure sufficient flexibility at minimum cost. The parameters of the smart contract are optimized such that the system's social welfare is maximized. The optimization problem is modeled as a mixed-integer linear program. Consequently, this article updates the unit-commitment (UC) formulation with the commitment aspects of DR units. The proposed extension to the UC problem considers the critical aspects of DR participation, such as: the total length of interruptions within a window, the frequency of interruptions within a time-window irrespective of their length, and the net energy deviation from the original load profile. Deployment of the smart DR contract in the unit dispatch problem requires translating DR participants' characteristics to their equivalent aspects in conventional thermal generators, such as minimum up time, minimum down-time, start-up and shutdown costs. The obtained results demonstrate significant improvement in social welfare, notable reduction of curtailed renewable energy and reduction in extreme ramping events of conventional generators.
The optimal deployment of heterogeneous energy storage (HES), mainly consisting of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multienergy systems. Consequently, this article proposes a risk-averse method for HES deployment in a residential multienergy microgrid (RMEMG), considering the diverse uncertainties and multienergy demand-side management (DSM). Apart from the HES size and location planning, its optimal investment phase is also determined by maximizing the system equivalent daily profit (EDP) and minimizing the risk. To handle the system uncertainties from renewable energy sources, power demands, outdoor temperature, and residential hot water needs, the multistage adaptive stochastic optimization approach is utilized. Then, through the constraint linearization and stochastic scenario sampling, the original nonlinear deployment model is converted to a mixed-integer linear programming one and tested on an IEEE 33-bus distribution network based RMEMG. The effectiveness of the proposed method is verified by comparing it with the existing practices. The comparison results indicate that the proposed risk-averse deployment method can effectively increase the system EDP and more immune to the uncertainties. Besides, this method can be practically applied for the emerging RMEMGs, such as smart buildings, intelligent homes, etc., which get long-term DSM contracts.
The rapid development of distributed energy resources (DER) in the distribution grid calls for novel control and coordination solutions. Optimal management of DER will enable end-users to decrease their electricity costs and provide crucial services to grid operators. In this paper, a decentralized Optimal Power Flow (OPF) model is used to locally coordinate DER in distribution networks, while considering the network constraints, in a distributed, transparent and secure fashion. To achieve that, a consensus-based distributed optimization algorithm is developed using the general form Alternating Direction Method of Multipliers (ADMM). To enable transparent and verifiable management of the network, the paper provides a comprehensive procedure for the implementation of the decentralized OPF on a private blockchain-smart contracts platform. The performance of the proposed framework is tested using real data from a case study in a residential neighborhood in Amsterdam with different varieties of DER. The implementation procedure on a blockchain-smart contracts platform may be adopted in other problems that require a smart contract to act as a virtual aggregator.