Distributed Demand Response Research Articles

Agent-based distributed demand response in district heating systems

Current district heating systems are moving towards 4th generation district heating in which end-users play an active role in system operation. Research has shown that optimising buildings' heating demands can release network congestion and contribute to reducing primary energy usages. Coupled with end-user privacy concerns, an approach in which buildings jointly optimise their heating demands while preserving privacy needs to be investigated. In view of this need, we have developed a distributed demand response approach based on exchange ADMM to support distributed agent-based heating demand optimisation for the district heating system with minimal private information exchanges. This paper summarises mathematical derivation, simulation and implementation of the proposed approach. The results show that the proposed approach obtained the same results as its centralised counterpart proposed in the existing literature and the sensible information exchanges were substantially reduced. An implementation at multiple spatial scales and time scales on micro-controllers and a communication system validates the proposed approach in a practical context. In conclusion, the proposed approach is suitable for real-world implementation in a large-scale district heating system.

Aggregation and remuneration in Demand-Response with a blockchain-based framework

This article describes the possibility of using the blockchain technology for load and generation aggregation in a new distributed demand response (DR) service and customer remuneration system. The blockchain technology and the use of smart contracts for DR allow the creation of a distributed system, in which customers can communicate directly, in a transparent, secure, and traceable way, with the grid operator to provide their flexibility. In this article, the DR problem formulation takes into account several aspects, which are periodically executed. First, the blockchain records customers’ energy consumption or production; then, the smart contract starts calculating the baseline and the potential support provided by each customer to fulfill the requested load adaptation. Customers’ availability for generation and load profile modulation is also taken into account, as well as their privacy and an updated definition of the roles of grid and market operators in a new DR scenario supported by the blockchain technology. The blockchain used is Hyperledger Fabric, since it turned to be flexible for smart contract implementation while supporting multitenancy. Results show the possibility of successfully applying the blockchain technology to this particular topic, even considering privacy-preserving issues.

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

The use of Distributed Ledger Technologies such as Blockchain for certifying Demand Response services allows for the creation of a distributed system in which customers can communicate with the system operator to provide their flexibility, in a secure, transparent and traceable way. Blockchain technology also supports incentive mechanisms for users taking part in the service through the generation of utility tokens to recognize the user’s contribution. This paper presents the experimental test of a novel methodology for Demand Response programs implementation by using the Blockchain technology. The latter is employed for defining a distributed Demand Response service and a new system for its tracing and certification. For this work, a Smart Contract has been conceived and written to execute Demand Response events, calculate users’ baseline, compute the support provided by each user towards the fulfilment of the requested load curve modification and remunerate each user with utility tokens proportionally to their contribution. To test the methodology, a Hyperledger Fabric network and a Smart Contract were deployed on four nodes of the Microgrid Laboratory of the Department of Energy Technology at Aalborg University (DK). Subsequently, a realistic scenario comprising two consumer nodes was developed using power electronic converters for generating the household profiles and Smart Meters for the measurement of the consumption profiles. Theoretical and experimental results show the feasibility of Distributed Ledger Technologies in smart grids management with a minimum investment in new hardware while enabling the active participation of customers in Demand Response more transparently and fairly.

Emission-Aware and Cost-Effective Distributed Demand Response System for Extensively Electrified Large Ports

A large part of the electric power demand of commercial ports is particularly flexible. Nowadays, the most significant flexible loads of a commercial port are the refrigerated containers, known as reefers, and the shore-side electric power supplies for ships. Moreover, ports usually have a great potential for local energy generation from renewable energy sources. The real-time control of the numerous port flexible loads is difficult to be achieved with conventional control systems. Multi-agent systems (MAS) have been proved to be very efficient in complex systems and, as it is shown in this paper, they can also be effective for large ports. In this paper, a real-time distributed demand response system using MAS with hierarchical structure is proposed for extensively electrified large ports. The proposed control system ensures real-time limitation of ship emissions, and the reduction of operation cost and fast power variations caused by local wind parks. To achieve the above, the electric power demands of in-port reefers and shore-to-ship power supplies are suitably controlled. A fuzzy-logic-based system is exploited to estimate reefers’ desired utilization factor and integrated to the developed MAS. The efficiency of the proposed method is evaluated by detailed simulations of a realistic case study of a large commercial port.

Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.

Identifying Flexible Pool Pumps Suitable for Distributed Demand Response Schemes

Demand response will be an important tool as non-dispatchable generation is added to the grid. Swimming pool filtration pumps are a promising appliance for the grid operator to control because, unlike air conditioners, their time of operation can be shifted by a day without affecting their primary role. Although many customers set and forget the timer settings, this paper studies smart meter data and demonstrates that many change the settings frequently. A “care index” is proposed that quantifies how much a customer appears to care about the precise timing of pump operation, which indicates how willing they are likely to be to relinquish control to the grid operator.

Decentralized price incentive energy interaction management for interconnected microgrids

Interconnection of wide-area microgrids encounters a lot of challenges in energy management. Energy interaction among microgrids requires more extensibility and flexibility in communication and decision-making compared to energy management of a single microgrid. To address the problem, two distributed interaction algorithms for both islanded and grid-connected modes are proposed here, where price signal motivates aggregators to adjust power consumption and generation. Privacy-preserving gossip algorithm is designed to avoid disclosing local privacy in islanded interaction. Demand response (DR), as an important means of interaction, can optimize resource distribution and realize virtuous and stable market operation in grid-connected mode. Perturbation primal-dual sub-gradient method is therefore designed to optimize distributed demand response (DDR) considering global predetermined inequal constraints. Finally, taking an actual multiple-microgrid system as an example, simulation verifies the effectiveness and robustness under communication uncertainties.

Enabling cyber‐physical demand response in smart grids via conjoint communication and controller design

This study proposes a novel user-centric cyber-physical framework to achieve distributed demand response (DDR) in a distribution system where local schedulers (LS) present at individual buses program both local and non-local loads to reduce the maximum load within a sparse communication setting. A COnjoint Methodology for communication and controller DESign (COMDES) has been proposed to design the topology of the communication network while considering various cyber and physical properties of the smart grid like bandwidth, load profile and voltage controller stability margin. The designed communication network provides a unified pathway for exchanging multiple types of data among different points in the grid to consummate the advanced ideas of DDR and multi-input multi-output voltage control concurrently. Concepts of Lyapunov stability and linear matrix inequalities are employed to design optimisation frameworks for tuning the values of voltage controllers which, in turn, play a vital role in pooling the loads to be scheduled by the individual LS for execution. Established algorithms like earliest deadline first, least laxity first and dynamic rate priority are used to generate switching schedules for redistributed loads and achieve peak shaving. IEEE 4 bus and 14 bus models have been used to demonstrate the efficacy of the proposed framework.

On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid

By locally solving an optimization problem and broadcasting an update message over the underlying communication infrastructure, demand response program based on the distributed optimization model encourage all users to participate in the program. However, some challenging issues present themselves, such as the existence of an ideal communication network, especially, when utilizing wireless communication, and the effects of communication channel properties, like the bit error rate, on the overall performance of the demand response program. To address the issues, this paper first defines a cloud-based demand response (CDR) model, which is implemented as a two-tier cloud computing platform. Then a communication model is proposed to evaluate the communication performance of both the CDR and distributed demand response models. This paper shows that when users are finely clustered, the channel bit error rate is high and the user datagram protocol (UDP) is leveraged to broadcast the update messages, making the optimal solution unachievable. Contradictory to UDP, the transmission control protocol will be caught up with a higher bandwidth and increase the delay in the convergence time. Finally, this paper presents a cost-effectiveness analysis which confirms that achieving higher demand response performance incurs a higher communication cost.

Energy management of smart micro-grid with response loads and distributed generation considering demand response

Abstract Governmental incentives to develop clean and renewable energy sources, and concerns about the increasingly serious environmental pollution problem, and the development of smart grid technology are the most important motivations for adding distributed generations to conventional power systems and carrying out the Demand Response program (DR). In these circumstances, it has become a key factor for the development of micro-grid to realize efficient and economical operation of smart micro-grid considering Demand Response strategy. In this paper, an intelligent park micro-grid consisting of photovoltaic power generation, combined cooling heating and power system, energy storage system and response load is modeled to study the optimal scheduling strategy of these units by taking into account the price-based demand response. To achieve this goal, an optimization model for the economic operation of micro-grid is established, and the model presented mainly aims to minimize the operating cost of micro-grid system and make full use of clean energy under the premise of considering distributed power generation and demand response. This operation optimization problem is solved by the Genetic Algorithm (GA) and the best solution on the best operating strategy is determined by the clean energy resources and demand response program. Finally, a micro-grid project in China was used to carry out optimization simulation in order to verify the accuracy and reliability of the established model. It is found that the operation optimization model of micro-grid with demand response can effectively reduce the operation cost of and improve the utilization rate of renewable energy sources.

A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids

This paper proposes a two-level interactive mechanism to address the day-ahead scheduling optimization of smart distribution networks with multi-microgrids. Each microgrid realizes the economic dispatch in the lower level interactive model, where the uncertainties caused by renewable energy integration and loads as well as the coordination of controllable distributed generator, energy storage and demand response are handled through a two-stage robust model. In the upper level interactive model, the distribution network operator (DNO) guarantees the operational quality by considering the power flow constraints and decides the transaction price and quantity with each microgrid based on the solutions of the lower level problem, to minimize the operation cost. The interaction process is formulated as a Stackelberg game, in which the trading strategies of coupled microgrids, acted as followers, are influenced by price incentive mechanism of DNO and affect the decision of DNO in turn. A collaborative strategy promoting the internal power trading among microgrids is also presented to deal with the market power of DNO, such that the participating microgrids can benefit from the internal trading and further reduce the cost. Numerical cases on an improved IEEE 33-bus system show the effectiveness of the proposed model and solution strategy.

Stochastic Thermal Load Management

There has been much recent interest in distributed demand response—where loads adjust in response to shortfalls (or excesses) in power supply—in order to maintain power quality, reduce dispatch costs, and even prevent or ameliorate blackouts. Some of the most promising candidates for demand response are thermal loads, such as heaters, air conditioners, and refrigeration units, which can often be switched OFF temporarily without inconveniencing the user. However, naive attempts to control such loads have experienced difficulties due to devices becoming synchronized in an undesirable way. Recent work using stochastic controls has been shown to be very promising, but still has certain practical limitations. In particular, these controls exhibit poor robustness and undesirable nominal device behavior. In this paper, we propose a novel stable closed-loop stochastic thermal controller that shows much greater robustness than the state of the art algorithm and provides nominal behavior very similar to that of ordinary hysteretic control. Our controller allows a large population of devices to react to the same demand response signal while maintaining an even distribution of device states. This allows them to act in unison to closely approximate a single deterministically controlled load.

An Optimal and Distributed Demand Response Strategy for Energy Internet Management

This study proposes a new model of demand response management for a future smart grid that consists of smart microgrids. The microgrids have energy storage units, responsive loads, controllable distributed generation units, and renewable energy resources. They can buy energy from the utility company when the power generation in themselves cannot satisfy the load demand, and sell extra power generation to the utility company. The goal is to optimize the operation schedule of microgrids to minimize the microgrids’ payments and the utility company’s operation cost. A parallel distributed optimization algorithm based on games theory is developed to solve the optimization problem, in which microgrids only need to send their aggregated purchasing/selling energy to the utility company, thus avoid infringing its privacy. Microgrids can update their operation schedule simultaneously. A case study is implemented, and the simulation results show that the proposed method is effective and efficient.

Demo abstract: testbed for distributed demand response devices—internet of things

As the drive to integrate renewable resources into the modern power grid grows, services such as demand response (DR) capable of providing ancillary services (AS) for grid stabilization has become a central focus of current research. While DR programs are effective in theory, utilities are reticent to implement because of lack of data. The next step is to provide a platform to test proposed mechanisms to move toward implementation. This work presents a complete package for internet-of-things hardware-in-the-loop simulation offering scalability in simulation, integration with real and simulated device nodes and full control over grid parameters. It is a DR testbed providing telemetry and actuation for connected loads which, itself, serves as a node in a larger software based PSIM power simulation. Simulated node’s characteristics are modeled from the real node’s data. Increasing penetration of intermittent renewable generation resource challenges grid operators in providing stability to the grid and balancing generation to meet load [1–3]. In addition to improving social welfare [4] DR has been shown to be a viable solution to providing a portion of AS which is able to contribute to stabilizing grid conditions [5–7]. The testbed is used to test a DR response algorithm published previous at the REDlab for grid frequency control using DR [8].

Communication-Efficient Distributed Demand Response: A Randomized ADMM Approach

In this paper, we consider the distributed demand response (DDR) problem for achieving the real-time power balance in a neighborhood with a large number of load customers and renewable energy sources (RES). While most of the existing DDR schemes require iterative information exchange between the customers and the load aggregator through two-way communications, this paper studies the DDR schemes that rely on neighbor-wise communication between customers only. Such DDR schemes can be realized by low-cost wireless networks. To this end, we propose the use of a randomized alternating direction method of multipliers to develop a fully DDR algorithm. Notably, the proposed DDR algorithm is communication-efficient because it can yield promising power balance performance using a few times of neighbor-wise message exchanges. Moreover, the proposed DDR algorithm does not need synchronization between customers and is robust against random communication errors. For performing online demand response control, we combine the proposed DDR algorithm with the rolling-window-based model-predictive control method, and simple load and RES forecasting methods. By using real solar power data, we demonstrate via simulations that the proposed DDR algorithm improves the real-time power balance substantially and outperforms the existing DDR schemes that use the subgradient method for optimization.

Overgrid: A Fully Distributed Demand Response Architecture Based on Overlay Networks

In this paper, we present Overgrid , a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed demand response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. As overlay networks in communications establish logical links between peers regardless of the physical topology of the network, the Overgrid is able to apply some power balance criteria to its system of buildings, as they belong to a virtual microgrid, regardless of their physical location. We exploit an innovative distributed algorithm, called flow updating, for monitoring the power consumption of the buildings and the number of nodes in the network, proving its applicability in an Overgrid scenario with realistic power profiles and networks of up to 10 000 buildings. To quantify the energy balance capability of Overgrid, we first study the energy characteristics of several types of buildings in our university campus and in an industrial site to accurately provide some reference buildings models. Then, we classify the amount of “flexible” energy consumption, i.e., the quota that could be potentially exploited for DR programs. Finally, we validate Overgrid emulating a real P2P network of smart buildings behaving according to our reference models. The experimental results prove the feasibility of our approach.

Fast distributed demand response algorithm in smart grid

This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning of each time slot, each end-user U+002F energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption U+002F generation locally. The proposed demand response algorithm converges rapidly to a consumption U+002F generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user U+002F energysupplier estimates its energy consumption U+002F generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.

Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

Thermostatically controlled appliances (TCAs) have great thermal storage capability and are therefore excellent demand response (DR) resources to solve the problem of power fluctuation caused by renewable energy. Traditional centralized management is affected by communication quality severely and thus usually has poor real-time control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation (OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals.

Providing frequency regulation reserve services using demand response scheduling

During power grid contingencies, frequency regulation is a primary concern. Historically, frequency regulation during contingency events has been the sole responsibility of the power utility. We present a practical method of using distributed demand response scheduling to provide frequency regulation during contingency events. This paper discusses the implementation of a control system model for the use of distributed energy storage systems such as battery banks and electric water heaters as a source of ancillary services. We present an algorithm which handles the optimization of demand response scheduling for normal operation and during contingency events. We use dynamic programming as an optimization tool. A price signal is developed using optimal power flow calculations to determine the locational marginal price of electricity, while sensor data for water usage is also collected. Using these inputs to dynamic programming, the optimal control signals are given as output. We assume a market model in which distributed demand response resources are sold as a commodity on the open market and profits from demand response aggregators as brokers of distributed demand response resources can be calculated. In considering control decisions for regulation of transient changes in frequency, we focus on IEEE standard 1547 in order to prevent the safety shut-off of inverter-based generation and further exacerbation of frequency droop. This method is applied to IEEE case 118 as a demonstration of the method in practice.