Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEnergy Trading in Microgrids for Synergies among Electricity, Hydrogen and Heat Networks
80
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The emerging paradigm of interconnected microgrids advocates energy trading or sharing among multiple microgrids. It helps make full use of the temporal availability of energy and diversity in operational costs when meeting various energy loads. However, energy trading might not completely absorb excess renewable energy. A multi-energy management framework including fuel cell vehicles, energy storage, combined heat and power system, and renewable energy is proposed, and the characteristics and scheduling arrangements of fuel cell vehicles are considered to further improve the local absorption of the renewable energy and enhance the economic benefits of microgrids. While intensive research has been conducted on energy scheduling and trading problem, a fundamental question still remains unanswered on microgrid economics. Namely, due to multi-energy coupling, stochastic renewable energy generation and demands, when and how a microgrid should schedule and trade energy with others, which maximizes its long-term benefit. This paper designs a joint energy scheduling and trading algorithm based on Lyapunov optimization and a double-auction mechanism. Its purpose is to determine the valuations of energy in the auction, optimally schedule energy distribution, and strategically purchase and sell energy with the current electricity prices. Simulations based on real data show that each individual microgrid, under the management of the proposed algorithm, can achieve a time-averaged profit that is arbitrarily close to an optimum value, while avoiding compromising its own comfort.
rate research
Read More
To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions. Day-ahead stochastic energy trading between the distribution system operator (DSO) and MGs is conducted in the first stage of a centralized retail market, where a chance-constrained uncertainty distribution locational marginal price (CC-UDLMP) containing the cost of uncertainty precautions is used to settle transactions. In the second stage, a novel intra-day peer-to-peer-based (P2P) flexibility transaction pattern is implemented between MGs in local flexibility markets under the regulation of DSO to eliminate power imbalances caused by rolling-based estimates whilst considering systematic operations. A fully distributed iterative algorithm is presented to find the equilibrium solution of this two-stage sequential game framework. Moreover, in order to enhance the versatility of this algorithm, an improved Lp-box alternating direction methods of multipliers (ADMM) algorithm is used to efficiently resolve the first-stage stochastic economic dispatch problem with a mixed-integer second-order cone structure. It is verified that the proposed market mechanism can effectively improve the overall market efficiency under uncertainties.
Efforts to efficiently promote the participation of distributed energy resources in community microgrids require new approaches to energy markets and transactions in power systems. In this paper, we contribute to the promising approach of peer-to-peer (P2P) energy trading. We first formalize a centralized welfare maximization model of an economic dispatch with perfect information based on the value of consumption with zero marginal-cost energy. We characterize the optimal solution and corresponding price to serve as a reference for P2P approaches and show that the profit-maximizing strategy for individuals with storage in response to an optimal price is not unique. Second, we develop a novel P2P algorithm for negotiating energy trades based on iterative price and quantity offers that yields physically feasible and at least weakly Pareto-optimal outcomes. We prove that the P2P algorithm converges to the centralized solution in the case of two agents negotiating for a single period, demonstrate convergence for the multi-agent, multi-period case through a large set of random simulations, and analyze the effects of storage penetration on the solution.
The coal industry contributes significantly to the social economy, but the emission of greenhouse gases puts huge pressure on the environment in the process of mining, transportation, and power generation. In the integrated energy system (IES), the current research about the power-to-gas (P2G) technology mainly focuses on the injection of hydrogen generated from renewable energy electrolyzed water into natural gas pipelines, which may cause hydrogen embrittlement of the pipeline and cannot be repaired. In this paper, sufficient hydrogen energy can be produced through P2G technology and coal-to-hydrogen (C2H) of coal gasification, considering the scenario of coal district is rich in coal and renewable energy. In order to transport the mined coal to the destination, hydrogen heavy trucks have a broad space for development, which can absorb hydrogen energy in time and avoid potentially dangerous hydrogen injection into pipelines and relatively expensive hydrogen storage. An optimized scheduling model of electric-gas IES is proposed based on second-order cone programming (SOCP). In the model proposed above, the closed industrial loop (including coal mining, hydrogen production, truck transportation of coal, and integrated energy systems) has been innovatively studied, to consume renewable energy and coordinate multi-energy. Finally, an electric-gas IES study case constructed by IEEE 30-node power system and Belgium 24-node natural gas network was used to analyze and verify the economy, low carbon, and effectiveness of the proposed mechanism.
This paper proposes a joint input and state dynamic estimation scheme for power networks in microgrids and active distribution systems with unknown inputs. The conventional dynamic state estimation of power networks in the transmission system relies on the forecasting methods to obtain the state-transition model of state variables. However, under highly dynamic conditions in the operation of microgrids and active distribution networks, this approach may become ineffective as the forecasting accuracy is not guaranteed. To overcome such drawbacks, this paper employs the power networks model derived from the physical equations of branch currents. Specifically, the power network model is a linear state-space model, in which the state vector consists of branch currents, and the input vector consists of bus voltages. To estimate both state and input variables, we propose linear Kalman-based dynamic filtering algorithms in batch-mode regression form, considering the cross-correlation between states and inputs. For the scalability of the proposed scheme, the distributed implementation is also presented. Complementarily, the predicted state and input vectors are leveraged for bad data detection. Results carried out on a 13-bus microgrid system in real-time Opal-RT platform demonstrate the effectiveness of the proposed method in comparison with the traditional weighted least square and tracking state estimation methods.
The goal of this paper is the experimental validation of a gray-box equivalent modeling approach applied to microgrids. The main objective of the equivalent modeling is to represent the dynamic response of a microgrid with a simplified model. The main contribution of this work is the experimental validation of a two-step process, composed by the definition of a nonlinear equivalent model with operational constraints, adapted to the microgrid environment, and the identification procedure used to define the model parameters. Once the parameters are identified, the simplified model is ready to reproduce the microgrid behavior to voltage and frequency variations, in terms of active and reactive power exchanges at the point of common coupling. To validate the proposed approach, a set of experimental tests have been carried out on a real LV microgrid considering different configurations, including both grid-connected and islanded operating conditions. Results show the effectiveness of the proposed technique and the applicability of the model to perform dynamic simulations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
