Use Of Demand Response Research Articles

Integrated energy cluster hierarchical regulation technology considering demand response

Integrated Energy Cluster (IEC), the regional aggregation of integrated energy systems (IES), has accumulated plenty of dispatchable resources with the development of energy market. This, while significantly providing system Demand Response (DR) potential, also complicates the interaction of the IEC with the main grid and increases the difficulty of system scheduling. To address this issue, this paper proposes a Reinforcement Learning-driven multi-agent hierarchical regulation framework that makes full use of DR to maximize the benefits of both IEC and main grid. Firstly, in the context of the DR market, a mechanism for IECs to bid in the real-time DR market is proposed. Furthermore, an "IEC-main network" hierarchical regulation model taking account of DR is established to minimize the IEC operation cost and maximize the societal benefit. Moreover, an optimization algorithm utilizing Deep Deterministic Policy Gradient (DDPG) with Multi-process (MP) and Priority Experience Replay (PER) mechanism is proposed to allow adaptability to high-latitude and large-scale applications. In case study, the proposed model and algorithm is tested on an 8-node system and a 24-node system. The result indicates that the hierarchical regulation model considering DR can improve the system economy by 2.59 % than that without DR and the improved DDPG algorithm can enhance training effectiveness in comparison with DDPG and PER-DDPG.

Combined peak reduction and self-consumption using proximal policy optimisation

Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal policy optimisation (PPO), have tried to increase data efficiency. Additionally, combining RL with transfer learning has been proposed in an effort to mitigate this challenge. In this work, we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline. We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff. We show our adapted version of PPO, combined with transfer learning, reduces cost by 14.51% compared to a regular hysteresis controller and by 6.68% compared to traditional PPO.

Smart contracts and homomorphic encryption for private P2P energy trading and demand response on blockchain

Blockchain technology offers great value in terms of decentralization, data integrity, transparency, and traceability, however the transactional data is public, and accessible raising concerns about violating privacy regulations. For example, in the peer-to-peer energy trading and demand response use cases, the data stored in blockchain may allow a third party to infer the load profiles or even identify the behind the meter assets. In this paper, we employ homomorphic techniques to encrypt the energy transactional data stored on the blockchain allowing the smart contracts functions responsible for implementing the business logic of the energy flexibility trading and settlement to perform computations on encrypted data. As computations on smart contracts and public blockchains can be expensive, we have used the lighter version of the Partial Homomorphic Encryption scheme to obfuscate the energy data. To ensure the validity of the smart contracts' functions executed on encrypted data, we leverage on the consensus mechanism of the blockchain network, thus ensuring computation correctness. The solution was validated considering a micro-grid with 12 prosumers that trade their flexibility peer-to-peer (P2P). The results demonstrate the feasibility of maintaining encrypted energy data on the blockchain, executing smart contract functions on encrypted data, and preserving the privacy of computations. As anticipated, the trade-off for better privacy is the gas consumption overhead of the smart contracts’ functions which is higher compared to the non-encrypted case, depending on the length of the public-private keys pair. Nonetheless, our solution exhibits consistent execution times for smart contracts, making it suitable for private networks where gas costs are of minimal concern.

Analysis of the impact of demand response on the Norwegian energy system

AbstractEuropean CO2 reduction goals have led to an increase in variable energy sources such as wind and solar, and consequently to an energy system that will need more flexibility in the future. In Norway, the hydropower reservoirs will enable the country to play a crucial role in European electrification by delivering flexibility to countries in Northern Europe. A further source of flexibility is demand response (DR) accumulated in residential, commercial, and industrial sectors. The paper discusses DR, load shifting, and load shedding based on the application of a stochastic TIMES model and it evaluates the role of DR in the Norwegian energy system towards 2050. The analysis shows that cost-efficient DR operation primarily comes from space heating in residential buildings. The use of DR, which is season-dependent, increases the volume of electricity trade, including electricity export and import to neighboring countries, and it smooths electricity prices. The implementation of DR in Norway leads to decreases in expected electricity price and total system cost by exporting flexible electricity and importing low price electricity. Additionally, it affects hydropower and reservoir management.

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

Aggregate Impact Analysis of Demand Response Programs, Electric Vehicles, and Combined Heat and Power Units on Integrated Management of Industrial Virtual Power Plant

Efficient utilization of existing energy resources within smart industrial grids constitutes an indispensable part of any energy management system (EMS). In this regard, virtual power plants (VPPs) play a crucial role in smart exploitation of available generated power. The present paper offers a new and exciting perspective at the EMS of Industrial VPPs (IVPPs). The grid management simultaneously makes use of demand response (DR) loads and electric vehicles (EVs) deployed at parking spaces. The EMS follows the objective of maximizing overall profit of its assemblage. At the same time, the EMS endeavors to augment the grid reliability at peak conditions and reduce load shedding of industrial centers. Naturally, there are uncertainties in such parameters as electricity market prices, EVs, and renewable energy production sources. Thus, a random-based energy management problem approach is adopted. To validate, the proposed method is experimented on the second zone of the modified IEEE-RTS standard network. The simulations reveal that the EVs’ presence in dedicated parking spaces can bring about a dramatic increase in the storage capacity of the IVPPs. This causes a reduction in the use of network’s overall power capacity. In fact, a prominent feature of the EMS is constant use of DR programs and selecting best one for each IVPP at different time hours. On the whole, the adopted procedure gives rise to a general reduction in network’s operational costs, considerable de-peaking as well as enhanced performance of EVs at parking spaces.

Cost optimization of a microgrid considering vehicle-to-grid technology and demand response

Demand response (DR) programs can offer various benefits especially in microgrid environments with renewable energy systems (RESs) and energy storage technologies when effectively planned and managed. Accordingly, this study proposes an energy management approach for a neighborhood including residential end-users with photovoltaic (PV) systems, a shared energy storage system (ESS) and an electric vehicle (EV) fleet. The proposed approach presents a novel energy credit mechanism (ECM) for the EV fleet and households separately to exploit the EV batteries and store the excess PV energy in the neighborhood through the shared ESS for later use. End-users gain energy credits before a DR event and use these credits during the peak periods to minimize their total energy cost (TEC), resulted in a decrease in the peak demand. Also, the energy credits gained by the EV fleet are used through the vehicle-to-home (V2H) and vehicle-to-grid (V2G) services with the same objective. In order to conduct a more realistic analysis, a battery degradation cost estimation model is employed and the uncertain behavior of the EV users is considered. The case studies show that the proposed optimization strategy has the capability of considerably reducing the energy costs and peak demand.

Negawatt Planning for Aggregation Coordinator Considering Fluctuations in Electricity Demand of Consumers

Power suppliers generate electricity to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. Demand response is attracting attention as a solution to these problems in recent years with the spread of renewable energy, energy resources such as storage batteries, and the development of IoT technologies. Demand response means changing the power demand pattern by controlling demand side energy resources and distributed energy resources. In recent years, the use of demand response and the construction of mechanisms for it are being promoted. In this study, we focus on negawatt trading that is one of the demand response policies. We formulate a stochastic programming model for negawatt planning operation and the efficient solution method are shown. From experiment results, it is shown that the worst situation can be avoided using CVaR model. We also showed the efficiency of solution.

Business Models for Demand Response: Exploring the Economic Limits for Small- and Medium-Sized Prosumers

The European energy transition increasingly requires flexibility to ensure reliable operation of the electricity system, making use of demand response, a promising concept. With technological advances in the fields of big data analysis and the internet of things, small- and medium-sized prosumers could also provide flexibility services through aggregators. A lot of conceptual work has been conducted recently to formulate business models in this context, but their viability still remains unclear. In this paper, a quantitative validation is conducted of two business models that are frequently proposed in the scientific discussion. The aim of this work is to explore the economic limits of these business models and show under which conditions they can be profitable for small- and medium-sized prosumers. For this purpose, a multi-level contribution margin calculation for several scenarios, customer segments and target markets is conducted. The results show that the profitability for the participation of small loads is still very low under current market conditions. Especially for household consumers, transaction costs are too high to be covered by the revenues. Considering the quantitative results, in the future profitable business cases can only be expected for medium-sized tertiary consumers.

Security‐constrained optimal scheduling and operation of island microgrids considering demand response and electric vehicles

As an independent unit, microgrid operator (MGO) should respond to the energy demand of customers with an optimal provision cost. Apart from the ability to guarantee the security of microgrid (MG) operation, the MGO should consider the greenhouse emission effects as well. This paper investigates a novel energy management system for an islanded MG. In the proposed strategy, two efficient methodologies are used to maximize the profit and security of the MG: (a) the optimal operation of electric vehicles (EVs) in vehicle-to-grid (V2G) mode and (b) the use of demand response (DR) program. Besides, a hierarchical control structure is proposed to manage frequency and voltage uncertainties in a permissible range. Improving the reliability of the MG is another goal of the proposed strategy, which is pursued by the studied controlling framework. The proposed approach is evaluated through a set of simulation and GAMS tests. Results indicate that the frequency deviation of the MG is damped through the proposed management strategy and place the spotlight on the effectiveness of the approach.

Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in order to Increase the Amount of Renewable Energy in the Grid

Demand response plays a very important role in balancing the intermittent production of an increasing share of renewable energy sources on the energy market. This article analyses the importance of demand response and the role of aggregators for the new development of the electricity market, where the renewables will play a more important role. The main objective of this research is to determine the acceptance level of demand response and its implementation on the energy consumer side. This acceptance should include a professional actor, the aggregator which is assuming the role of optimizing the relation between energy producers and consumers, and to monitor the implementation and use of demand response. The research is based on semi-structured interviews with experts in energy from Hungary, Romania and Serbia, on workshops with experts and a wider online survey with end customers for electricity. The results indicate that there is a willingness potential to implement demand response programs with aggregators as intermediaries between energy providers and end consumers of electrical energy.

An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities

Demand response (DR) is widely recognized as an alternative approach to achieving the supply-demand balance of power systems. The accurate quantification of DR flexibility potential is important for implementing DR, especially in countries where fewer DR related studies have been conducted and where the societal structures determining DR flexibility are different from the US and Europe. This study designs an interdisciplinary framework by integrating household characteristics based on population census, activity-based energy demand modeling, and a survey on the behavioral intention to participate in DR and shift energy use. This study quantifies the DR flexibility potential of washing machines and dryers used in 18.9 million households in the Kanto region in Japan with a peak electricity demand of 57 GW. These appliances are estimated to be capable of increasing electricity demand by 70–220 MW, depending on the time of day when DR is activated for 60 min. This potential decreases with the duration of DR. The estimated potential per household is much smaller in Japan than in Europe. The results highlight the importance of promoting DR to the majority of populations, particularly among the underserved populations, and addressing the behavioral and cultural barriers to promoting DR.

Load flexibility potential across residential, commercial and industrial sectors in Brazil

The flexibility in grid operations has become a valuable solution to respond to the several problems brought about by the growth of intermittent renewable generation. The use of Demand Response (DR) measures has emerged as a promising option to tackle these challenges. However, to the best of the authors’ knowledge, the assessment of the theoretical DR potential has not yet been fully addressed in the available literature. This paper presents a first attempt to establish the theoretical load flexibility potential for the Brazilian power sector and includes two central issues: (1) a sectoral assessment across the residential, commercial and industrial sectors and (2) a regional analysis by broadly splitting up the Brazilian power system into four main subsystems. Our findings reveal that the overall maximum hourly theoretical DR potential in Brazil is expected to double, increasing from 12.8 GW in 2017 to almost 25.6 GW by 2050. Although the majority of the demand-side response potential seems to lie in the industrial sector, a lower but still substantial potential for the residential and commercial sectors has been also identified. The evidence from this study supports the hypothesis that the commercial sector will have a key role in offering the most valuable load flexibility potential (i.e. when the power system mostly needs). The high expected increase in the overall electricity DR potential for the residential sector is driven mainly by the increase in both the number of households and the number of appliances per house. However, large amounts of flexible loads in the residential sector might not be necessarily available during on-peak times. Findings of this research may be of great assistance for future analyses to identify, for example, the extent to which DR can cost-effectively compete with other supply-side options.

Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

<p>This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.</p>

Use of demand response for voltage regulation in power distribution systems with flexible resources

In low-voltage power distribution systems with high penetration of photovoltaics (PVs) generation and electric vehicles (EVs), the over-voltage problem arises at times because of large PV generation, and under-voltage problem also arises sometimes because of simultaneous charging of massive EVs. Over- and under-voltage problems lead to more difficulties in achieving voltage regulation. Demand response (DR) is expected to be promising and cost-effective in promoting smart grids, and hence, the utilisation of flexible resources (FRs) through DR can be helpful for distribution system voltage regulation. This study introduces a hierarchical control structure of a community energy management system (CEMS) and multiple sub-CEMSs to apply an FR-based two-stage voltage regulation technique. In the first stage, i.e. the day-ahead scheduling stage, each sub-CEMS optimises the FRs' schedules for minimising customers' electricity cost and network voltage violation times. In the second stage, i.e. the real-time operation stage, the voltage sensitivity-based FRs' shifting method is proposed to eliminate network voltage violations caused by errors of estimated day-ahead data. The proposed models and methods are verified based on a realistic distribution system in Japan, where voltage violations, customer electricity cost and a number of on-load tap changer tap operations are proved to be reduced.

GOFLEX: towards a new integrated solution for a cost-effective use of demand response in distribution grids

GOFLEX: towards a new integrated solution for a cost-effective use of demand response in distribution grids

A Demand Response Approach to Scheduling Constrained Load Shifting

Demand response (DR) and its advantages are nowadays unquestionable due to the success of several recent implementations of DR programs. Improved methodologies and approaches are needed for the adequate consumers’ schedule in DR events, taking the consumers’ behaviour and preferences into account. In this paper, a virtual power player manages DR programs, minimizing operation costs, respecting the consumption shifting constraints. The impact of the consumption shifting in the target periods is taken into consideration. The advantages of the DR use in comparison with distributed generation (DG) are evaluated. Two scenarios based on 218 consumers in a frame of 96 periods have been implemented. It is demonstrated the advantages of DR in the operation of distributed energy resources, namely when considering the lack of supply.

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

Integration of Automatic Generation Control and Demand Response via a Dynamic Regulation Market Mechanism

Modern power systems utilize automatic generation control (AGC) for real-time frequency regulation to ensure power balance of generation and consumption. Increasing penetration of intermittent renewables will introduce greater variability in generation, which in turn requires an even higher participation of AGC. In light of this, frequency regulation becomes a very critical service in modern power systems’ operations that has to be carried out with high economic efficiency. The use of demand response (DR) is being offered as a green, affordable alternative to conventional generation-based AGC. Although advances in wide-area monitoring and control technologies have made real-time control of DR a possibility, significant challenges remain for large-scale integration. In this paper, we propose a dynamic regulation market mechanism (DRMM) that allows both DR units and generators to bid for regulation service in real time, ensuring optimal allocation and reducing regulation service costs. Unlike similar proposals in the literature, the DRMM is practical to implement on a large power system with multiple areas. In particular, the DRMM requires modest communication rates on par with the existing AGC system and can explicitly accommodate constraints on DR energy deferment. We demonstrate the added benefit of the DRMM over the conventional AGC through simulations on a 3-area 900-bus power system and derive conditions for its stability.

Impact of demand response on the optimal, techno-economic performance of a hybrid, renewable energy power plant

This paper presents a model to optimise the operation of a small-scale, grid-connected, hybrid power plant with and without demand response of the associated load. The model minimises the full cost of supplying electricity from a combination of solar photovoltaic, battery storage, a natural gas fuelled reciprocating engine generator and network delivered electricity, while satisfying several technical and environmental constraints. This includes the use of demand response, historical local solar irradiance and demand traces, constraints on solar photovoltaic, battery and engine performance, full electricity and natural gas tariffs, and the option to participate in the wholesale electricity market.The model is first applied to a school in Victoria, Australia as a case study. It is found that the optimal hybrid plant configuration depends strongly on the choice of greenhouse gas abatement target, electricity and natural gas tariffs, particularly tariffs with demand charges and the option to export electricity to the network grid. The school can also benefit from demand response, where savings result not only from the avoided network delivered electricity cost but also avoided hybrid plant capital investment and operation. Importantly, whilst demand response impacts the hybrid plant configuration it does not displace the optimality of hybrid power plants in distribution networks.