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// Call the function
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// > 8

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// Example can be run directly in your JavaScript console

// Create a function that takes two arguments and returns the sum of those
// arguments
var adder = new Function("a", "b", "return a + b");

// Call the function
adder(2, 6);
// > 8

Math

Lorem ipsum \(f(x) = x^2\).

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Highlights

• The application feasibility of hydrogen heavy trucks is discussed, in the typical scenario of coal districts.
• Hydrogen production becomes renewable by absorbing wind power, combining power-to-gas (P2G) technology and coal-to-hydrogen (C2H) of coal gasification.
• The efficient closed loop of hydrogen energy from generation to utilization is proposed, avoiding the potential hydrogen embrittlement of the pipeline.
• An optimized scheduling model of electric-gas IES is proposed based on second-order cone programming (SOCP).
• The feasibility, economy, low carbon, and effectiveness of the proposed integrated electric-gas system mechanism are analyzed.

Scenario

FIGURE 1. Coal district considering hydrogen heavy trucks and P2G equipment model of wind power to H2 and CH4.

As shown in Figure 1, the closed industrial loop between coal mining, hydrogen production, truck transportation of coal, and integrated energy systems has been innovatively proposed. In the coal mining process, part of the coal is used to produce hydrogen through C2H technology, and the other part is transported to power plants by hydrogen heavy trucks, which consume a lot of energy in the transportation process. Wind power is highly volatile and cannot be directly connected to the grid. Therefore, wind power produces hydrogen (energy for hydrogen heavy trucks) through electrolysis of water, and part of the hydrogen is further methanated to produce methane (injected into natural gas pipelines). Note that in this article, only the daily operating emissions (variable cost) is considered, and the emissions caused by the initial equipment investment (fixed cost) are not considered. Among them, coal is used for hydrogen production or transportation, and wind power is used for hydrogen production or methanation. By combining the needs of the coupled energy network, real-time optimized dispatching is carried out.

Power-to-Gas(P2G) Flow

FIGURE 2. Schematic diagram of actual flow and variable symbols of P2G equipment.

Comparison of heavy trucks with different power types

Large Tables

Code blocks

// Example can be run directly in your JavaScript console

// Create a function that takes two arguments and returns the sum of those
// arguments
var adder = new Function("a", "b", "return a + b");

// Call the function
adder(2, 6);
// > 8

Math

Lorem ipsum \(f(x) = x^2\).

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NOTE: You can add a message box.

Large text

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Large images

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Highlights

• A novel QoE-oriented uplink rate control and resource allocation schemes is propesed, for IoT using time-varying channels.
• Mean Opinion Score (MOS) model is designed to measure the degree of Quality of Experience (QoE).
• The original long-term optimization problem is converted into admission rate control subproblem and resource allocation subproblem in each time slot, based on the Lyapunov optimization.
• Gale-Shapley matching is utilized, with exploiting the special structure of the resource allocation subproblem.

Scenario

FIGURE 1. System model of M2M-based uplink IoT network.

As shown in Figure 1, we consider a cognitive Machine-to-Machine (M2M) network, which consists of a centralized Base station (BS), $K$ Cellular user equipments (CUEs), and $N$ M2M pairs.
Each CUE occupies one orthogonal spectrum sub-channel of equal bandwidth to perform uplink communication with the BS. The sets of CUEs and sub-channels are denoted as $\mathcal {C}= \lbrace C_1,C_2,$ $\ldots, C_k,$ $\ldots, C_K\rbrace$ and $\mathcal {S}=\lbrace S_1, S_2,$ $\ldots, S_k,$ $ \ldots, S_K\rbrace$, respectively. The sets of indices are denoted as $\mathcal {K}= \lbrace 1, 2,$ $\ldots, k,$ $\ldots, K\rbrace$.
Each M2M pair is composed of a M2M transmitter (MT) and a M2M receiver (MR). To implement the cognitive M2M communication, each M2M pair has to reuse the sub-channel allocated to a CUE. Denote the sets of M2M pairs as $\mathcal {M}= \lbrace M_1, M_2, $ $\ldots, M_n,$ $\ldots, M_N\rbrace$ and the sets of corresponding indices as $\mathcal {N}= \lbrace 1, 2,$ $\ldots, n,$ $\ldots, N\rbrace$. And the sets of MTs and MRs of M2M pairs are denoted as $\mathcal {MT}= \lbrace MT_1, MT_2, $ $\ldots, MT_n,$ $\ldots, MT_N\rbrace$, $\mathcal {MR}= \lbrace MR_1, MR_2, $ $\ldots, MR_n,$ $\ldots, MR_N\rbrace$, respectively.
A M2M pair is allowed to reuse the CUE's sub-channel for data dissemination if and only if certain constraints are satisfied, e.g., the rate and the delay constraints. Intuitively, a CUE is more willing to share its sub-channel with a M2M pair, which causes less interference to it and is more likely to refuse the request of a M2M pair, which causes serious interference.

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Algorithm

\begin{algorithm}[!htbp]
	\caption{Gale-Shapley-based resource allocation algorithm}
	\begin{algorithmic}[1]
		\STATE$\mathcal {M}$: The set of M2M pairs 
		\STATE$\mathcal {S}$: The set of subchannels
		\FOR{$n=1$ to $| \mathcal {M}|$}
		\STATE sort the subchannels of each M2M pair according to  $R_{n}(t)$ in decreasing order
		\ENDFOR
		\FOR{$k=1$ to $| \mathcal {S}|$}
		\STATE sort the M2M pair of each subchannel according to  $ \gamma_{C_{k}}(t)$ in decreasing order
		\ENDFOR
		\STATE Apply the Gale-Shapley Algorithm to find  $\omega_n^k$
		\IF{(the transmission rate for M2M pairs and the QoS for the cellular users can be both satisisfied)}
		\STATE the considered subchannel is assigned to the M2M pair
		\ELSE
		\STATE the considered subchannel cannot be assigned to the M2M pair
		\ENDIF
		\STATE The matching results of the n couples  are declared
	\end{algorithmic}
\end{algorithm}

Citation Map

This paper, on Google Scholar , has been cited 15 times, as of Sep. 6, 2022: