Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach

Research on energy management of hydrogen electric coupling system based on deep reinforcement learning

Focusing on the low-carbon economic operation of an integrated energy system (IES), this paper proposes a novel energy-carbon pricing and energy management method to promote carbon emission reductions in the IES based on the carbon emission flow theory and reinforcement learning (RL) approach. Firstly, an energy-carbon integrated pricing model is proposed. The proposed pricing method charges prosumers by tracing the embedded carbon emissions of energy usages, and establishes an energy-carbon-prices relationship between the power grid, IES and prosumers. Secondly, an energy management model considering the energy-carbon integrated pricing strategy is established based on the Markov decision processes (MDP), including prosumers energy consumption cost model and energy service provider (ESP) profit model. Then, a solving method based on the RL approach is proposed. Finally, numerical results show that the proposed method can improve operation economy and reduce carbon emissions of IES. When carbon price accompanying electricity and thermal is considered in the process of pricing and energy management, the profit of ESP can be improved and the cost of prosumers can be reduced, and the total carbon emission of IES can be reduced by 5.75% compared with not considering carbon price.

Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses

Manufacturers are scrambling for relief from today's energy expenses and price volatility. Most industry decision-makers believe the solution is to seek the lowest available energy prices. Too often, managers fail to grasp the opportunities offered by energy management, which focuses on both consumption and prices. Industry can be resistant to energy management for a variety of reasons. Simply put, energy management has no traditional place in the typical manufacturer's chart of organization, job descriptions, and performance accountabilities. While technology is fundamental to energy efficiency, it is people who make it work in an organizational context. DuPont, Frito-Lay, Unilever, and Kimberly-Clark are a few of the forward-thinking companies that have found ways to build energy management into their daily operations to positive effect. The Alliance to Save Energy (ASE) is documenting these companies' experiences in a series of case studies that refl ect the organizational and behavioral aspects of corporate-wide energy management. Case studies show that energy management motives and approaches are somewhat varied—there is no "one size fits all" solution. ASE offers a typology of industrial energy management strategies to illustrate the range of opportunities available to industry. Ultimately, it is a manufacturer's organizational character that determines its ability to manage energy consumption. A checklist included in this article allows the reader to diagnose a manufacturer's aptitude for undertaking various energy management strategies.

With the widespread installation of smart meters, more and more residents are signing long-term contracts with the grid for demand response (DR) scheme. As an effective way of managing energy consumption about energy prices and system energy balance conditions, the DR scheme contributes to the system cost-saving. In this context, the optimal operation of a multi-energy residential microgrid (MERMG) is studied considering the multi-energy DR scheme. The objective is to minimize the net operating cost under the system’s operational constraints. In the multi-energy DR scheme, the price-based DR (PBDR) is used for power consumption and thermal (heat or cooling) inertia is for the thermal energy consumption. Besides, to guarantee the solution quality as well as relieve the computation burden, the original nonlinear items are linearized to make the operation model a mixed-integer linear programming (MILP) problem. Then it can be then efficiently solved by commercial solvers like Cplex and Gurobi. Finally, by applying the proposed method in the test MERMG and comparing it with the one without the multi-energy DR scheme, the validity of the proposed method is verified.

With the development of demand-side management in the smart grid, load-serving entity (LSE) plays a more important role for consumers, which purchases energy from the electricity market and sells it to consumers. Moreover, aggregated thermostatically controlled loads (TCLs) in smart buildings can provide additional demand response capacities and require efficient energy management methods. This article proposes a stochastic optimal energy management and pricing model for LSE with aggregated TCLs and energy storage based on the Stackelberg game and stochastic programming. The energy management and pricing problem are formulated as a bilevel optimization model. The upper level model aims to maximize LSE's expected profit under market price uncertainties and determines the offering prices to consumers. According to the offering price from upper level model, the lower level model optimizes the power purchasing pattern for consumers of two types of buildings with TCLs: factory and office buildings. The nonlinear bilevel model is reformulated and converted into mixed-integer linear programming using a strong duality theory. The proposed model is validated by numerical studies based on real market prices from the PJM electricity market. In addition, the impacts of energy storage, number of buildings, comfortable indoor temperature limits, and offering price limits on LSE's profit are analyzed.

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

Integrated energy systems (IESs) can realize the conversion and complementarity of various energy sources, which provides opportunities and challenges for the energy market. Considering that the user’s energy consumption is affected by the energy price difference, there is a problem that the new energy output in the comprehensive energy system does not fully match the user’s energy demand period. In order to solve the above problems, this paper proposes a two‐stage optimization model of “open source and reducing expenditure” to give full play to the potential of multiple energy sources on the load side to participate in demand response (DR) and combine low‐carbon technology and market mechanisms to realize the low‐carbon economic operation of the comprehensive energy system. In the first stage, a collaborative optimization strategy for electric and thermal DR is constructed from the aspect of “reducing expenditure,” a comprehensive load fuzzy DR mechanism based on the logistic function is constructed for electric load, and the load curve and time‐of‐use (TOU) energy price are optimized considering the coupling characteristics of user energy consumption, and nondominated sorting genetic algorithm (NSGA‐II) solution to achieve peak shaving and valley filling. In the second stage, a joint operation model of carbon capture power plant (CCPP) and power‐to‐gas (P2G) equipment is built from the aspect of “open source,” and the ladder‐type carbon trading mechanism is considered to rationalize the unit output and achieve low‐carbon emission reduction. The calculation results obtained through examples show that the total cost of the model is slightly reduced by 5.44%, but the actual total carbon emission of the system is greatly increased by 50.73%. It proves that the high‐carbon power plant transformation and TOU energy price optimization strategy are effective for the low‐carbon economic operation of the system and realize both economic benefits and benefits of the system.

Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of technologies, available at demand side, a proper energy manager with the support of a pricing profile forecaster is mandatory. Although energy management and price forecasting are recurrent topics, in literature they have been addressed separately. On the other hand, in this work, the aim is to investigate how well an energy manager is able to perform in presence of data uncertainty originating from the forecasting process. On purpose, an energy and resource manager has been revised and extended in the current manuscript. Finally, it has been complemented with a price forecasting technique, based on the Extreme Learning Machine paradigm. The proposed forecaster has proven to be better performing and more robust, with respect to the most common forecasting approaches. The energy manager, as well, has proven that the energy efficiency of the residential environment can be improved significantly. Nonetheless, to achieve the theoretical optimum, forecasting techniques tailored for that purpose may be required.

Amidst increasing global electricity consumption, governments prioritize energy efficiency and electric vehicle (EV) integration into energy markets. This study examines EV aggregator strategies employing smart charging methods, adjusting charging rates based on user preferences. Through simulations in Quito's distribution system, it analyzes actions' effects on aggregator costs and technical conditions, with a focus on demand response (DR) strategies, particularly in residential areas. Exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options, the study introduces a collaborative evaluation of dynamic pricing and peak power limiting-based DR strategies, integrating bi-directional EV and energy storage system (ESS) use. It also proposes a novel mixed-integer linear programming (MILP) model for home energy management (HEM), incorporating distributed renewable energy, V2H/V2G capabilities, two-way ESS energy trading, and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Electric vehicles (EVs) have a lot of potential to play an essential role in the smart power grid. EVs not only can reduce the amount of emission yielded from fossil fuels but also can be considered as an energy storage system (ES) and a backup system. EVs could support the demand response (DR) strategy that is considered as utmost importance to shift electricity demand in peak hours. This article aims to assess the impact of the presence of EV on DR strategy in a home-microgrid (H-MG). In order to reach the optimal set point, our energy management system (EMS) has been merged with differential evolution (DE) method. The results were auspicious and showed that the proposed method could decrease market clearing price (MCP) by 26% and increase the performance of DR by 17%.

With declining fossil fuel consumption and rising energy demand for renewable energy, the need for integration of these highly predictable sources into the electricity system increases. At the same time, there is a rise in the price of energy, which increases the willingness of consumers to change their breed in order to reduce the costs, or at least to keep them in an acceptable level. One of the options for optimizing energy savings on the consumer side is to use the principle of demand response. This principle enables the consumer, for example, to have the necessary information to optimize the consumption of electricity so as to minimize it when the energy price is high. In view of the constantly changing conditions in the electricity system, the need for optimization is to be implemented automatically, without the necessity of users of the system. This paper main focus is the formulation and optimization of Demand Side Management using the quasi-quadratic problem (MIQP). The result of such optimization is the use of individual devices that take into account the cost of electricity, the working cycle of the installation, the requirements of the user, the systems And limitations and other input information. The method proposed which, after implementation into the individual member - the energy manager - will ensure the optimal utilization of appliances and other Set up by the witches of a clever house.

In 2009, a pilot program was conducted to investigate the technical feasibility of bidding non-residential demand response (DR) resources into the California Independent System Operator's (CAISO) day-ahead ancillary services market as non-spinning reserves product. Three facilities, a retail store, a local government office building, and a bakery, were recruited into the pilot program and moved from automated price responsive programs to CAISO's participating load program. For each facility, hourly demand, and load curtailment potential were forecasted two days ahead and submitted to the CAISO the day before the trading day as an available resource. These DR resources were optimized against all other generation resources in the CAISO ancillary services market. Each facility was equipped with four-second real time telemetry equipment to ensure resource accountability and visibility to CAISO operators. When CAISO requests DR resources, OpenADR (Open Automated DR) communications infrastructure was utilized to deliver DR signals to the facilities' energy management and control systems. The pre-programmed DR strategies were triggered without a human in the loop. This paper describes the automated system architecture with detailed description of meter feedback in the DR signaling to maintain demand reduction at the government office building. The results showed that OpenADR infrastructure could be used for some products for the ancillary services market and DR strategies for heating ventilation and air conditioning and lighting provide fast enough response to participate in non-spinning reserve product in the ancillary services market.

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research.