Source-linked AI summary
Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant
Qing Yang, Hao Wang, Taotao Wang, Shengli Zhang, Xiaoxiao Wu, Hui Wang
TL;DR
Centralized VPP management raises privacy and trust concerns as it collects users’ private information and relies on an unverifiable coordinator. This paper develops a blockchain-based decentralized platform with distributed optimization and prototype implementation, reducing users’ total costs by 11.2% through energy trading.
Problem
Conventional VPP management collects users’ private energy information and relies on a centralized coordinator whose operation users cannot verify or trust.
Method
The paper designs a decentralized optimization algorithm for energy scheduling, trading, and network services, implements it in blockchain smart contracts, and develops a prototype system.
Results
11.2%: users’ total costs are reduced when users trade energy through the distributed energy management algorithm.
Takeaways & Limitations
The prototype demonstrates a blockchain-based VPP platform that supports transactive energy activities while preserving users’ privacy and enabling verifiable energy schedules.
Takeaways & Limitations
The centralized design remains vulnerable to single-point failure and requires trust in the coordinator.
Abstract
from arXiv · showhide
The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, \rv{brings} a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users' independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users' energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data-trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users' cost by up to 38.6% and reduces the overall system cost by 11.2%.
1. Introduction
DER penetration challenges centralized, unidirectional power systems, motivating decentralized VPP management. The paper combines decentralized optimization with blockchain to support verifiable, privacy-preserving VPP energy management and transactive energy activities.
- 1. Introduction: DER integration challenges centralized power systems and motivates decentralized systems with bidirectional power flow and VPP-based management.DERs include distributed renewables, storage, electric vehicles, and controllable loads.
- 1. Introduction: Centralized VPP coordination creates a black-box operation, privacy leakage, and a gap between theoretical methods and practical implementation.The coordinator collects users’ private energy information, computes schedules, and commands users to execute them.
- 1. Introduction: Blockchain is used as a trustable computing machine, secure communication tool, and payment mechanism for energy management rather than only for payments.The platform implements the energy management algorithm in smart contracts.
- 1. Related works: The work targets implementation on practical smart meters and considers both DER energy trading and grid services through aggregator–grid interaction.The stated services include energy trading and network services provided by DERs through the VPP.
- 1. Novelty and contribution: The platform supports energy trading and network services while optimizing system efficiency and preserving users’ privacy through a distributed algorithm.The contributions include energy scheduling, energy trading, network services, and a prototype system for validation.
2. System Model
The system model represents a VPP as interconnected smart houses containing diverse DERs and loads. It describes decentralized energy management across energy trading and grid-related services.
- 2. System Model: The platform models smart houses, VPP services, and blockchain-based decentralized energy management as three connected system components.The model introduces renewable generators, batteries, loads, energy trading, demand response, feed-in tariff, and ancillary service.
2.1. The smart house
The smart house combines renewable generation, grid supply, peer-to-peer trading, batteries, and diverse appliances under smart-meter scheduling. Its model captures supply constraints, user comfort, flexible-load scheduling, and battery operation.
- Power supply: Smart houses combine renewable generators, grid electricity, peer-to-peer trading, batteries, and appliances to meet household demand.Renewable output varies with environmental conditions, while users can also buy electricity from the grid or other VPP users.
- Power supply: Grid electricity is limited by each household’s fuse capacity and priced through a two-part tariff with higher peak pricing.The tariff is intended to encourage users to shave peak loads.
- Power supply: P2P trading represents purchases with positive pu,v[t] and sales with negative pu,v[t], while the VPP operator sets a fixed price below the normal grid price.The trading constraint balances bilateral exchanges across users and time slots.
- Electric appliances: Appliances are categorized as adjustable, time-shiftable flexible, or inflexible, with flexible loads constrained by total demand, per-slot bounds, and preferred schedules.Deviation from a preferred flexible-appliance schedule incurs a discomfort cost.
- Electric appliances: Air-conditioning operation is modeled through indoor temperature dynamics, environmental temperature, operating mode, and user discomfort relative to a preferred temperature.Indoor temperature is constrained to a reasonable range.
- Battery energy storage: Battery charge evolves from its previous level through charging and discharging, subject to efficiency, capacity, and power constraints.Aggregated batteries can store excess renewable energy and support appliances when needed.
2.2. The virtual power plant
The VPP connects smart-house users to provide feed-in tariff, demand response, and ancillary services. Users share trading decisions while independently scheduling their appliances in the decentralized management system.
- VPP services: The VPP provides feed-in tariff, demand response, and ancillary service mechanisms to participating smart-house users.These services respectively support renewable-energy sales, consumption adjustment, and battery-based grid support.
- Decentralized participation: Unlike a conventional centralized VPP, the proposed system requires users to share trading decisions while scheduling their appliances themselves.This arrangement is presented as decentralized VPP participation through smart meters.
- Feed-in tariff: Feed-in tariff rewards users for selling renewable energy to the grid, with FIT electricity bounded by remaining renewable generation.The grid is assumed to set a fixed FIT price.
- Demand response: Demand response rewards users for reducing grid electricity usage, with prices varying by time slot and response bounded by scheduled grid consumption.Higher peak-hour prices can be used to achieve peak shaving.
- Ancillary service: Ancillary-service rewards incentivize users to reserve battery energy for load regulation or spinning reserves, subject to battery energy levels.The reserved energy can be dispatched to help maintain grid stability or recovery.
2.3. The blockchain system for the VPP energy management
The blockchain system supports decentralized VPP management through smart-meter nodes, peer-to-peer communication, consensus, and transactions for services, trading, and payments.
- Blockchain platform: Blockchain is used to create an open, verifiable, and decentralized VPP energy-management platform without a central coordinator.It also provides secure communication and digital currency for energy trading and service rewards.
- Network architecture: Users’ smart meters join the blockchain network as nodes, connecting to the grid through power lines and to the network through communication links.The connected meters form a peer-to-peer network that transmits blockchain messages via gossip.
- Consensus protocol: The system adopts proof-of-authority consensus because its low computational complexity suits smart meters and its confirmation time supports VPP network services.A selected PoA committee generates blocks, while normal nodes send transactions without generating blocks.
- Transactions: Blockchain transactions carry network-service information for FIT, demand response, and ancillary service participation.These transactions are made between VPP users and the grid.
- Transactions: P2P energy-trading transactions record trading information, while token transfers support user payments and operator rewards.The detailed blockchain implementation is discussed later in the paper.
3. Problem Formulation of the Virtual Power Plant
The paper formulates standalone and cooperative VPP energy-management problems for residential users. Standalone users optimize local operation, while cooperative users jointly schedule resources and trade electricity, creating cross-user optimization dependencies.
- 3. Problem Formulation of the Virtual Power Plant: The formulation compares standalone and cooperative VPP modes, with cooperative users trading electricity and exchanging information to increase VPP profit.
- 3.1. The standalone (SA) mode VPP: In standalone mode, each user maximizes local reward by managing grid and renewable supplies, appliances, batteries, and network services.The services include feed-in tariff, demand response, and ancillary service.
- 3.1. The standalone (SA) mode VPP: Standalone scheduling includes adjustable, flexible, and inflexible appliances alongside renewable generation, grid supply, battery operation, and FIT, DR, and AS services.The vector notation is defined in Table 1, and the objective is expressed as an optimization over these variables.
- 3.1. The standalone (SA) mode VPP: Each smart house must balance power consumption and supply throughout the operational horizon.Consumption includes appliance loads, battery charging, and demand response; supply includes renewable generation, grid energy, and battery discharging.
- 3.1. The standalone (SA) mode VPP: The standalone operational cost equals overall payment minus overall rewards, and each user minimizes it by selecting an energy schedule.The schedule includes grid supply, renewable generation, appliance loads, battery charging and discharging, and network services.
- 3.1. The standalone (SA) mode VPP: Because the standalone objective is convex, users can solve their optimization problems locally with standard convex optimization tools.The resulting standalone schedule serves as the benchmark for cooperative-mode comparison.
- 3.2. The cooperative (CO) mode VPP: Cooperative users jointly schedule appliances and network services while exchanging surplus energy through peer-to-peer trading.Trading purchases are added to each user’s operational cost, and the resulting schedule includes inter-user trading decisions.
- 3.2. The cooperative (CO) mode VPP: The cooperative optimization cannot be solved locally by one user because each user’s trading solution depends on other users’ energy-usage schedules.The paper therefore introduces a subsequent distributed algorithm to solve the coupled problem.
4. The Decentralized Energy Management Algorithm for Virtual Power Plant
The proposed algorithm replaces centralized VPP coordination with privacy-preserving primal-dual optimization implemented across users’ smart meters and a blockchain smart contract. Local scheduling and blockchain-based dual updates iterate until convergence.
- 4. The Decentralized Energy Management Algorithm for Virtual Power Plant: The conventional VPP coordinator globally minimizes all users’ operational costs but requires centralized access to their energy-management information.Its solution represents the optimal schedules for all VPP users.
- 4. The Decentralized Energy Management Algorithm for Virtual Power Plant: Centralized management exposes users’ preferences, generation capability, battery information, and complete energy-usage schedules to the coordinator.The approach also makes the coordinator’s computation a black box that users cannot verify or trust.
- 4. The Decentralized Energy Management Algorithm for Virtual Power Plant: The proposed decentralized algorithm preserves privacy while achieving optimal energy management without requiring a centralized coordinator.Users share only energy-trading decisions, not grid usage, renewable usage, appliance loads, battery operations, DR, FIT, or AS information.
- 4.1. The augmented Lagrangian method: ADMM-based primal-dual decomposition introduces auxiliary trading variables to overcome coupling constraints between users’ energy-trading decisions.The augmented Lagrangian uses a penalty coefficient ρ/2 and dual variables λu,v.
- 4.2. The primal-dual decomposition: Each user solves the primal optimization problem locally, while the dual problem updates shared auxiliary and dual variables.The primal problem updates schedules and trading decisions; the dual problem uses them in the next iteration.
- 4.2. The primal-dual decomposition: The iterations converge to the original optimization problem’s optimal solution when the convergence error is sufficiently small.The convergence criteria use the distance between auxiliary and original trading variables and the change in dual variables across iterations.
- 4.3. Algorithm implementation: The smart contract receives users’ trading decisions, computes auxiliary and dual variables, and returns them for the next local iteration.The process repeats until the convergence threshold is met, after which users obtain their optimal schedules.
- 4.3. Algorithm implementation: Private operating data remains on users’ smart meters, while only energy-trading decisions are sent to the smart contract in each iteration.The local primal problem covers grid, renewable, appliance, battery, demand-response, feed-in, and ancillary-service variables.
5. System Implementation and Evaluation
The paper presents a blockchain-based VPP platform and simulation results designed to validate the proposed algorithm and system.
- 5. System Implementation and Evaluation: The evaluation covers the developed blockchain-based VPP energy management platform, including its experiment setup, blockchain system, and algorithm implementation.
5.1. Blockchain-based VPP energy management platform
The platform uses a proof-of-concept smart-meter network built from NanoPi devices and an Ethereum-derived blockchain. PoA consensus supports the embedded hardware, while real-world energy data evaluates the algorithm’s feasibility and performance.
- 5.1.1. Experiment setup: The proof-of-concept network uses 15 NanoPi Neo2 boards to emulate smart meters on a private Ethernet network.Each board has a quad-core 1.5GHz ARM-A53 CPU, 1GB memory, a 16GB SD card, and Ubuntu Core 16.04.
- 5.1.2. The blockchain system: Ethereum is selected because it is mature, supports smart contracts, and can be modified through its open-source Go implementation.
- 5.1.2. The blockchain system: PoA replaces PoW because PoW exhausts the NanoPi’s CPU and memory, whereas PoA consumes substantially fewer hardware resources.
- 5.1.2. The blockchain system: The blockchain network contains 5 PoA nodes and 10 normal nodes, operates under a 250Kbps bandwidth limit, and averages about 200 TPS.The observed peak throughput is 780 TPS.
- 5.1.2. The blockchain system: The embedded-device hardware requirements can be met by inexpensive devices and modern smart meters.
- 5.1.3. The implementation of Algorithm 1: Using GNU Octave on NanoPis with convergence thresholds ϵ1 and ϵ2 set to 0.000001, Algorithm 1 converges within 40 iterations.The result is presented as evidence that the method is feasible in practice.
5.2. Performance evaluation
Using real-world data over a one-week simulation, the decentralized VPP algorithm produced coordinated schedules for power supply, appliances, network services, and peer-to-peer trading. The results show complementary grid and renewable use, diverse appliance and service schedules, and lower costs under coordinated trading.
- Evaluation setup: The evaluation used real-world solar, wind, consumption, and temperature data over a one-week, 168-hour simulation.Battery capacities were randomly generated between 10kWh and 15kWh.
- Power supply scheduling: Grid and renewable supply were well complemented, reducing users’ dependence on the grid and utilizing renewable energy effectively.The two-part tariff also produced flat grid-supply plateaus associated with peak-shaving.
- Appliance scheduling: Adjustable appliance loads followed outdoor-temperature trends, while flexible loads showed diverse schedules reflecting users’ renewable generation and preferences.The schedules distinguish adjustable appliances from flexible appliances.
- Network services: Users provided feed-in energy, ancillary services, and demand response through schedules aligned with low demand, idle battery capacity, and evening peak periods.Extra renewable generation was sold to the grid, batteries supplied reserve, and adjustable appliances reduced consumption during peaks.
- P2P energy trading: Complementary trading profiles enabled users to buy and sell energy according to differing generation and load patterns.The example includes one user buying during daytime and selling at night, while another consistently bought and a third had surplus energy.
- Cost reduction and payment: 11.2% was the reduction in the sum of all users’ costs when coordinated energy trading was used instead of independent scheduling.The comparison considered SA mode without energy trading and CO mode with Algorithm 1-based trading.
6. Conclusion and future work
The paper developed and evaluated a blockchain-based VPP platform combining distributed optimization, energy trading, network services, and blockchain implementation. Future work targets greater decentralization and scalability to systems with hundreds or thousands of users.
- Contributions: The platform combines distributed energy-trading algorithm design with blockchain-system implementation for residential VPP management.It manages energy schedules, trading, and network services for users with loads, storage, and local renewables.
- Evaluation: The platform was evaluated through experiments and simulations using real-world data, demonstrating effective management of VPP schedules, trading, and network services.The evaluation also demonstrated the effectiveness of the blockchain system.
- Future work: Future work will remove centralized communication and computation to make the VPP more decentralized and flat.This is identified as a direction for further platform improvement.
- Future work: The authors plan to test VPPs with hundreds or thousands of users using more efficient distributed algorithms.The larger-system scope is explicitly framed as future work.