Provide Demand Response Services Research Articles

Model predictive control with self-learning capability for automated demand response in buildings

This paper presents an optimal management strategy, called Building Optimizer, based on a Model Predictive Control (MPC) approach with self-learning capabilities for buildings. The proposed MPC is a key enabler of cooperative demand response strategies at community level, ensuring the allocation of an optimal demand profile at each participating member of the community according to an optimal consumption reference defined by a complementary agent at community level. In this way, the proposed solution can contribute to provide demand response services exploiting the unlocked residential flexibility. The MPC calculates the optimal setpoints of the HVAC system’s terminal units, considering the expected usage of the buildings and the outdoor conditions, and exploiting the building’s thermal inertia. The models embedded in the MPC are grey-box models representing a thermal zone of the building. An adaptive MPC is proposed to mitigate the high uncertainty present in building management due to the time-variant characteristics of the building and the uncertain internal heat gains due to occupancy and other disturbances. For that, the reduced models incorporate self-learning capabilities implemented as Moving Horizon Estimators that perform a continuous calibration based on real-time measurements. This solution allows full automation for model calibration and management of the terminal units. This paper presents a case study to assess the thermal comfort assurance and power flexibility provision potential of the proposed solution. First, the Building Optimizer is compared to a regular benchmark consisting of an on–off controller, evaluating the performance of each controller for indoor temperature tracking. For the assessment of the flexibility provision, a baseline MPC with fixed model parameters obtained by an offline calibration is used for comparison to the Building Optimizer with self-learning capabilities. Different flexibility scenarios are simulated, considering different restrictions regarding thermal comfort and various flexibility request signals. Notably, the Building Optimizer outperforms the baseline MPC in all scenarios, particularly guaranteeing thermal comfort. Incorporating self-learning capabilities enhances controller performance by mitigating the effect of uncertainty, allowing the Building Optimizer to shift up to 13.08% of peak periods into valley periods, and tracking a flexibility request scenario of 10% with no discomfort.

Preemptive scheduling of EV charging for providing demand response services

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.

Preemptive Scheduling of Ev Charging for Providing Demand Response Services

Preemptive Scheduling of Ev Charging for Providing Demand Response Services

Blockchain Technology Applied to Energy Demand Response Service Tracking and Data Sharing

Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

Evaluation of advanced control strategies of electric thermal storage systems in residential building stock

This paper investigates the effect of different control strategies applied to electric thermal storage systems to provide demand response services. These results indicate how policymakers or manufacturers could target the implementation of advanced control on electric thermal storage systems and apply these to households characterised by different occupancy profiles, thereby making demand response initiatives more attractive to end users.

Quantifying the Impact of Solar Photovoltaic and Energy Storage Assets on the Performance of a Residential Energy Aggregator

Demand response (DR) and renewable energy sources have opened new avenues for end-users to lower their energy expenses via energy management systems. Aggregators facilitate the participation of end-users by acting on their behalf and interacting with bulk electricity markets. In this paper, an energy management algorithm is presented to investigate the impact of distributed photovoltaic (PV) and central energy storage system (ESS) assets on the economic performance of an energy aggregator in the residential sector. To enable DR, the aggregator provides a competitive incentive price to end-users, and centrally optimizes the central ESS assets and schedule of committed customer elastic loads. Thus, customers reduce their daily electricity bill while the aggregator decreases the aggregated peak consumption and earns profits as a return for providing DR services. The scope of this paper pertains to the economic impact of distributed PV and central ESS assets on aggregator profits and customer savings resulting from DR, including ESS degradation. Simulation results showed that the central ESS increases the income of the aggregator, whereas residential PV reduces the impact of DR.

Micro Water–Energy Nexus: Optimal Demand-Side Management and Quasi-Convex Hull Relaxation

This paper investigates the water network's potential ability to provide demand response services to the power grid under the framework of a distribution-level water-energy nexus (micro-WEN). In particular, the hidden controllability of water loads, such as irrigation systems, was closely studied to improve the flexibility of electrical grids. A optimization model is developed for the demand-side management (DSM) of micro-WEN, and the simulation results assert that grid flexibility indeed benefits from controllable water loads. Although the proposed optimal DSM model is an intractable mixed-integer nonlinear programming (MINLP) problem, quasi-convex hull techniques were developed to relax the MINLP into a mixed-integer convex programming (MICP) problem. The numerical study shows that the quasi-convex hull relaxation is tight and that the resulting MICP problem is computationally efficient.

Improving the benefits of demand response participation in facilities with distributed energy resources

Demand response has proven to be a distributed energy resource of great potential over the last decades for electrical systems operation. However, small or medium size facilities generally have a very limited ability to participate in demand response programs. When a facility includes several generation resources, energy storage systems, or even demand flexibility, the decision-making becomes considerably harder because of the amount of variables to be considered. This paper presents a method to facilitate end users' decision-making in demand response participation. The method consists of an algorithm that uses demand and generation forecasts and costs of the available resources. Depending on the energy to be reduced in a program, the algorithm obtains the optimal schedule and facilitates decision making, helping end users to decide when and how to participate. With this method, end users' capability to participate in these programs is clearly increased. In addition, the method is contrasted by simulations based on real programs developed at the Campus de Vera of the Universitat Politècnica de València. The simulations carried out show that the developed method allows end users to take advantage of the potential of their facilities to provide demand response services and obtain the maximum possible benefit.

Coordinating the operations of smart buildings in smart grids

With big thermal storage capacity and controllable loads such as the heating ventilation and air conditioning systems, buildings have great potential in providing demand response services to the smart grid. However, uncoordinated energy management of a large number of buildings in a distribution feeder can push power distribution systems into the emergency states where operating constraints are not completely satisfied. In this paper, we propose a bi-level building load aggregation methodology to coordinate the operations of heterogeneous smart buildings of a distribution feeder. The proposed methodology not only reduces the electricity costs of buildings but also guarantees that all the distribution operating constraints such as the distribution line thermal limit, phase imbalance, and transformer capacity limit are satisfied.

A European perspective of the development of deep geothermal in urban areas

AbstractThe future of our energy supply systems is moving towards Smart Cities and Smart Rural Communities, where the integration of combined technologies using renewable energy sources reduces environmental impact and offers citizens a better quality of life. Geothermal has a particularly important role in smart electricity and thermal grids, since it can deliver both heating and cooling, and electricity.Shallow geothermal, using heat pumps, is a key energy source for smart energy systems. It provides solutions for the future energy system by coupling smart thermal and electricity grids with underground thermal storage and by providing reliable and affordable heating and cooling supply to both urban and rural areas.As these technologies can be installed in grid and off‐grid heating and cooling systems, they perfectly fit the new smart cities and rural communities approach.In addition, there is also an important role for shallow geothermal energy in connection with and management of smart electricity grids. Geothermal heat pumps can provide demand response services, thereby contributing to grid stabilisation, whilst Underground Thermal Energy Storage (UTES) is an excellent storage solution. Shallow geothermal technologies will be utilised in the next generation of district heating: Smart Thermal Grids.

A multi-agent system providing demand response services from residential consumers

High share of distributed energy resources (DER) into power systems can significantly modify the net demand profile. Error forecasts of intermittent generation of renewable energy sources (RES) along with the current inelastic behavior of the consumption can provoke considerable network operational issues, such as frequency fluctuations and voltage imbalances. Increasing the noncontrollable DER penetration in network operation requires increased flexible and dispatchable generation capacity for balancing RES generation intermittency. Demand response mechanisms can be an efficient and less costly alternative for handling the grid issues posed by high RES penetration. The aim of this paper is to demonstrate enabling Information and Communication Technologies (ICT) and operational tools for distributed demand management mechanism that allows consumers to participate in grid support without affecting their level of satisfaction. An actual household environment called Mas Roig and located in Llagostera, Spain is used to demonstrate and assess the ICT based demand response mechanism. The results of the implementation are presented and evaluated providing useful insights for a mass deployment of such mechanisms.