Hourly Demand Response Research Articles

Blockchain for Transacting Energy and Carbon Allowance in Networked Microgrids

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.

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.

The Effects of Demand Response on Security-Constrained Unit Commitment

This paper aims to study the effect of the hourly demand response (DR) on security-constraint unit commitment (SCUC) problem with considering economic and security objectives. The demand side participation can solve some of the electricity market problems. Here, DR is take into consideration as one of the demand side management (DSM) parts. The DR is consist of fixed and responsive loads. The fixed loads satisfy in any circumstance and responsive loads can curtail or shift to other operating hours. The combination of SCUC with DR is a complex and the mixed integer non-linear problem. The bender’s decomposition is used as an optimization technique for solve this problem. This technique solves the problem by decompose it into master and sub problems. One of the effective of this manner is reducing the processing time. The performance and effectiveness of the proposed method are demonstrated on 6-, 24- and 118-bus test systems.

Electricity-Natural Gas Operation Planning With Hourly Demand Response for Deployment of Flexible Ramp

This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.

Thermal Generation Flexibility With Ramping Costs and Hourly Demand Response in Stochastic Security-Constrained Scheduling of Variable Energy Sources

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations.

Hourly Demand Response in Day-Ahead Scheduling Considering Generating Unit Ramping Cost

This paper proposes a day-ahead scheduling model in which the hourly demand response (DR) is considered to reduce the system operation cost and incremental changes in generation dispatch when the ramping cost of thermal generating units is considered as penalty in day-ahead scheduling problem. The power output trajectory of a thermal generating unit is modeled as a piecewise linear function. The day-ahead scheduling is formulated as a mixed-integer quadratically constrained programming (MIQCP) problem with quadratic energy balance constraint, ramping cost, and DR constraints. A Lagrangian relaxation (LR) based method is applied to solve this problem. Numerical tests are conducted on a 6-bus system and the modified IEEE 118-bus system. The results demonstrate the merits of the proposed scheduling model as well as the impact of introducing ramping costs as penalty and DR as incentives in the day-ahead scheduling of power systems.

Hourly demand response in day‐ahead scheduling for managing the variability of renewable energy

This study proposes a stochastic optimisation model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general-purpose mixed-integer linear problem software is employed to solve the stochastic SCUC problem. The numerical results demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable RES.

SCUC With Hourly Demand Response Considering Intertemporal Load Characteristics

In this paper, the hourly demand response (DR) is incorporated into security-constrained unit commitment (SCUC) for economic and security purposes. SCUC considers fixed and responsive loads. Unlike fixed hourly loads, responsive loads are modeled with their intertemporal characteristics. The responsive loads linked to hourly market prices can be curtailed or shifted to other operating hours. The study results show that DR could shave the peak load, reduce the system operating cost, reduce fuel consumptions and carbon footprints, and reduce the transmission congestion by reshaping the hourly load profile. Numerical simulations in this paper exhibit the effectiveness of the proposed approach.